{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple Subplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes it is helpful to compare different views of data side by side.\n",
    "To this end, Matplotlib has the concept of *subplots*: groups of smaller axes that can exist together within a single figure.\n",
    "These subplots might be insets, grids of plots, or other more complicated layouts.\n",
    "In this section we'll explore four routines for creating subplots in Matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-white')\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``plt.axes``: Subplots by Hand\n",
    "\n",
    "The most basic method of creating an axes is to use the ``plt.axes`` function.\n",
    "As we've seen previously, by default this creates a standard axes object that fills the entire figure.\n",
    "``plt.axes`` also takes an optional argument that is a list of four numbers in the figure coordinate system.\n",
    "These numbers represent ``[left, bottom, width, height]`` in the figure coordinate system, which ranges from 0 at the bottom left of the figure to 1 at the top right of the figure.\n",
    "\n",
    "For example, we might create an inset axes at the top-right corner of another axes by setting the *x* and *y* position to 0.65 (that is, starting at 65% of the width and 65% of the height of the figure) and the *x* and *y* extents to 0.2 (that is, the size of the axes is 20% of the width and 20% of the height of the figure):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e487e5400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = plt.axes()  # standard axes\n",
    "ax2 = plt.axes([0.65, 0.65, 0.2, 0.2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The equivalent of this command within the object-oriented interface is ``fig.add_axes()``. Let's use this to create two vertically stacked axes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jCQIgCl/QU5bmcr54Noye/m5M3ZTChmM5Ukd6IFU1tYxfdZztKXm891hrXn5Y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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e203ba748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_axes([0.1, 0.5, 0.8, 0.4],\n",
    "                   xticklabels=[], ylim=(-1.2, 1.2))\n",
    "ax2 = fig.add_axes([0.1, 0.1, 0.8, 0.4],\n",
    "                   ylim=(-1.2, 1.2))\n",
    "\n",
    "x = np.linspace(0, 10)\n",
    "ax1.plot(np.sin(x))\n",
    "ax2.plot(np.cos(x));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have two axes (the top with no tick labels) that are just touching: the bottom of the upper panel (at position 0.5) matches the top of the lower panel (at position 0.1 + 0.4)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``plt.subplot``: Simple Grids of Subplots\n",
    "\n",
    "Aligned columns or rows of subplots are a common-enough need that Matplotlib has several convenience routines that make them easy to create.\n",
    "The lowest level of these is ``plt.subplot()``, which creates a single subplot within a grid.\n",
    "As you can see, this command takes three integer arguments—the number of rows, the number of columns, and the index of the plot to be created in this scheme, which runs from the upper left to the bottom right:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CiMLCQjF58mQxfvx4sXz5cqu2/fv3C7VaLQ4dOuTSOe7lC1+q8sVcU1NTO2Ro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\nnohIBljsiYhk4P8BcOyRN6ZCZI4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e20417ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(1, 7):\n",
    "    plt.subplot(2, 3, i)\n",
    "    plt.text(0.5, 0.5, str((2, 3, i)),\n",
    "             fontsize=18, ha='center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The command ``plt.subplots_adjust`` can be used to adjust the spacing between these plots.\n",
    "The following code uses the equivalent object-oriented command, ``fig.add_subplot()``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+f/755zh06BDy8vKgUCis5k+dOhWDBg1CUVGRw/voTnw9b1uY97/4atalpaVI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\n/wO9PuOW7CYrvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e200362e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
    "for i in range(1, 7):\n",
    "    ax = fig.add_subplot(2, 3, i)\n",
    "    ax.text(0.5, 0.5, str((2, 3, i)),\n",
    "           fontsize=18, ha='center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've used the ``hspace`` and ``wspace`` arguments of ``plt.subplots_adjust``, which specify the spacing along the height and width of the figure, in units of the subplot size (in this case, the space is 40% of the subplot width and height)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``plt.subplots``: The Whole Grid in One Go\n",
    "\n",
    "The approach just described can become quite tedious when creating a large grid of subplots, especially if you'd like to hide the x- and y-axis labels on the inner plots.\n",
    "For this purpose, ``plt.subplots()`` is the easier tool to use (note the ``s`` at the end of ``subplots``). Rather than creating a single subplot, this function creates a full grid of subplots in a single line, returning them in a NumPy array.\n",
    "The arguments are the number of rows and number of columns, along with optional keywords ``sharex`` and ``sharey``, which allow you to specify the relationships between different axes.\n",
    "\n",
    "Here we'll create a $2 \\times 3$ grid of subplots, where all axes in the same row share their y-axis scale, and all axes in the same column share their x-axis scale:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cx9Hc3Jz8fn8+6wYAZCHtNk5DQ4Nqa2sVDAZlWZbC4bAGBwdVVlam1tZWSf8O9J07d7pr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e20104748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 3, sharex='col', sharey='row')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that by specifying ``sharex`` and ``sharey``, we've automatically removed inner labels on the grid to make the plot cleaner.\n",
    "The resulting grid of axes instances is returned within a NumPy array, allowing for convenient specification of the desired axes using standard array indexing notation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pQUpKCgCgpaUFOTk5yMrKQk1Nja/nTUREbnDpnv1AgiAMauvs7EROTg50Oh3Cw8Mxffp0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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e20104748>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# axes are in a two-dimensional array, indexed by [row, col]\n",
    "for i in range(2):\n",
    "    for j in range(3):\n",
    "        ax[i, j].text(0.5, 0.5, str((i, j)),\n",
    "                      fontsize=18, ha='center')\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In comparison to ``plt.subplot()``, ``plt.subplots()`` is more consistent with Python's conventional 0-based indexing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``plt.GridSpec``: More Complicated Arrangements\n",
    "\n",
    "To go beyond a regular grid to subplots that span multiple rows and columns, ``plt.GridSpec()`` is the best tool.\n",
    "The ``plt.GridSpec()`` object does not create a plot by itself; it is simply a convenient interface that is recognized by the ``plt.subplot()`` command.\n",
    "For example, a gridspec for a grid of two rows and three columns with some specified width and height space looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grid = plt.GridSpec(2, 3, wspace=0.4, hspace=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this we can specify subplot locations and extents using the familiary Python slicing syntax:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fnx4xozLXz+98WWutNQ6Hw9i3bx8OHTqEkpISPSKqImW9cuUKhoeHUVNTgwMHDqC3txdN\nTU0JlzMnJwdLlizB8uXLkZqaig0bNqC/vz/hcnq9XixbtgxGoxHp6elYt24dPB6PLjm1zGSe4l72\nM7mcT0+R8t6+fRt79+7FvXv3AADd3d0oKCjQLauWuX5+58taa10uevz4cezevRulpaW65JssUtYt\nW7bgu+++w5dffomPP/4YVqsVdrs94XIaDAYsW7YMf/zxh7pfr9MjkXIuXboUXq8Xd+/eBQB4PB6s\nWLFCl5xaZjJPUV16OVszuZxPT5HyfvbZZzh37hwWLFiA1atX48iRI5AkSbesHo8HH374IW7cuAGD\nwQCz2QybzYbc3Fxdnt/5staPyllSUoJnnnkGa9asUR+7fft2VFZWJlzWiooK9TEDAwPq6cVEzOnz\n+fDmm29CURSsXLkSjY2Nul3OGinnF198AbfbjdTUVKxZswZvvPGGLhmB2M/2nJQ9ERHpizcvISIS\nAMueiEgALHsiIgGw7ImIBMCyJyISAMueiEgALHsiIgGw7ImIBMCyJyISAMueiEgALHsiIgGw7ImI\nBBBV2ff19aG8vBxnz559aN/ly5exc+dOVFZW4pNPPlE/39TUhMrKSlRVVeHXX3+NXWIiihnOtjgM\nWg+4c+cO3n//fWzYsGHa/UePHkVzczPMZjNqa2uxefNmDA8Pw+fzweVywev1wm63w+VyxTw8Ec0c\nZ1ssmq/sZ3LT2/l2024iEXG2xaL5yt5gMMBgmP5h09301u/3Y2RkBFardcrng8GgekeYu3fvwuPx\nwGQyITU1dbbfA5HQxsfHEQwGUVhY+MgbVE+Hsz3/zHStgSjKPhb+e38Uj8eDmpqaufjSRMJobW3F\nunXr5vRrcrb1MZO1nlXZP+qmt2lpaRFv6DzxcWtrKywWy2wiEAnv1q1bqKmpmTJjs8XZTkyzWetZ\nlf3km95aLBZcvHgRTqcTIyMj+Oijj1BVVTXtDZ0n3t5ZLBbk5ubOJgIR/b9YnjbhbCe2may1Ztn/\n96a3bW1tU25629jYiMOHDwMAtm3bhry8POTl5cFqtaKqqkq9qS8RJRbOtlh0ueH4wMAANm3ahPb2\ndv70J5qlRJqnRMqSjGbz/PI3aImIBMCyJyISAMueiEgALHsiIgGw7ImIBMCyJyISAMueiEgALHsi\nIgGw7ImIBMCyJyISAMueiEgALHsiIgGw7ImIBMCyJyISAMueiEgAUd2pqqmpCT09PZAkCXa7HUVF\nRQAe3KqsoaFBfZzf78fhw4chyzIOHjyIgoICAMDKlStx5MiROMQnopniXItFs+y7urrg8/ngcrng\n9Xpht9vhcrkAAGazGS0tLQCA+/fv48UXX4TNZoPH48H69etx6tSp+KYnohnhXItH8zROZ2cnysvL\nAQD5+fkYHR1FOBx+6HHffPMNNm/ejIULF8Y+JRHFFOdaPJplHwqFkJOTo24bjUYEg8GHHvfVV19h\n586d6vb169dRV1eH6upqdHR0xCguEcUC51o8UZ2zn2y6W9ZevXoVTz31lHqX+RUrVuDAgQPYunUr\n/H4/du3ahQsXLiA9PX32iYko5jjXyU/zlb0sywiFQup2IBCAyWSa8phLly5hw4YN6rbZbMa2bdsg\nSRKWL1+OxYsXY3BwMIaxiWg2ONfi0Sz74uJitLW1AQB6e3shy7L6k37Cb7/9hlWrVqnb58+fR3Nz\nMwAgGAxiaGgIZrM5lrmJaBY41+LRPI2zdu1aWK1WVFVVQZIkOBwOuN1uZGVloaKiAsCDhV+0aJF6\njM1mQ0NDA9rb2zE2NobGxka+1SNKIJxr8UR1zn7yNbcApvy0B4Bvv/12ynZmZiZOnz49y2hEFE+c\na7HwN2iJiATAsiciEgDLnohIACx7IiIBsOyJiATAsiciEgDLnohIACx7IiIBsOyJiATAsiciEgDL\nnohIACx7IiIBsOyJiATAsiciEkBUf+K4qakJPT09kCQJdrsdRUVF6j6bzQaLxYLU1FQAgNPphNls\njngMEemPcy0WzbLv6uqCz+eDy+WC1+uF3W6Hy+Wa8pgzZ85Muft8NMcQkX441+LRPI3T2dmJ8vJy\nAEB+fj5GR0cRDodjfgwRzR3OtXg0yz4UCiEnJ0fdNhqNCAaDUx7jcDhQXV0Np9MJRVGiOoaI9MO5\nFk9U5+wnUxRlynZ9fT2effZZZGdnY//+/epNjCMdQ0SJhXOd/DTLXpZlhEIhdTsQCMBkMqnbO3bs\nUD8uLS1FX1+f5jFEpC/OtXg0T+MUFxerP9V7e3shyzIyMzMBALdv38bevXtx7949AEB3dzcKCgoi\nHkNE+uNci0fzlf3atWthtVpRVVUFSZLgcDjgdruRlZWFiooKlJaWorKyEgsWLMDq1auxZcsWSJL0\n0DFElDg41+KRFB1OvA0MDGDTpk1ob29Hbm7uXH95oqSSSPOUSFmS0WyeX/4GLRGRAFj2REQCYNkT\nEQmAZU9EJACWPRGRAFj2REQCYNkTEQmAZU9EJACWPRGRAFj2REQCYNkTEQmAZU9EJACWPRGRAFj2\nREQCYNkTEQkgqnvQNjU1oaenB5IkwW63o6ioSN135coVnDx5EikpKcjLy8OxY8fQ3d2NgwcPoqCg\nAACwcuVKHDlyJD7fARHNCOdaLJpl39XVBZ/PB5fLBa/XC7vdDpfLpe5/99138fnnn8NisaC+vh4/\n/PADMjIysH79epw6dSqu4YloZjjX4tE8jdPZ2Yny8nIAQH5+PkZHRxEOh9X9brcbFosFAGA0GjEy\nMhKnqEQUK5xr8WiWfSgUQk5OjrptNBoRDAbV7YkbDgcCAXR0dKCsrAwAcP36ddTV1aG6uhodHR2x\nzk1Es8C5Fk9U5+wnm+6WtUNDQ6irq4PD4UBOTg5WrFiBAwcOYOvWrfD7/di1axcuXLiA9PT0mIQm\notjiXCc/zVf2siwjFAqp24FAACaTSd0Oh8PYt28fDh06hJKSEgCA2WzGtm3bIEkSli9fjsWLF2Nw\ncDAO8YloJjjX4tEs++LiYrS1tQEAent7Icuy+hYPAI4fP47du3ejtLRU/dz58+fR3NwMAAgGgxga\nGoLZbI51diKaIc61eDRP46xduxZWqxVVVVWQJAkOhwNutxtZWVkoKSnBuXPn4PP58PXXXwMAtm/f\njueffx4NDQ1ob2/H2NgYGhsb+VaPKIFwrsUT1Tn7hoaGKdurVq1SP/Z4PNMec/r06VnEIqJ441yL\nhb9BS0QkAJY9EZEAWPZERAJg2RMRCYBlT0QkAJY9EZEAWPZERAJg2RMRCYBlT0QkAJY9EZEAWPZE\nRAJg2RMRCYBlT0QkAJY9EZEAWPZERAKI6u/ZNzU1oaenB5IkwW63o6ioSN13+fJlnDx5EqmpqSgt\nLcX+/fs1jyEi/XGuxaJZ9l1dXfD5fHC5XPB6vbDb7XC5XOr+o0ePorm5GWazGbW1tdi8eTOGh4cj\nHkNE+uJci0ez7Ds7O1FeXg4AyM/Px+joKMLhMDIzM+H3+5GdnY0nnngCAFBWVobOzk4MDw8/8hgA\nGB8fBwDcunUrLt8UkUgm5mhirqIRj7menIGzHR8zWesJmmUfCoVgtVrVbaPRiGAwiMzMTASDQRiN\nxin7/H4/RkZGHnkM8OBmxQBQU1Pz2IGJaHrBYBBPPvlkVI+Nx1xPZAA42/H2OGs9Iapz9pMpivK4\nhzx0TGFhIVpbW2EymZCamvrY/x4R/c/4+DiCwSAKCwtn/G/EYq4Bzna8zWatNctelmWEQiF1OxAI\nwGQyTbtvcHAQsiwjLS3tkccAQEZGBtatW/fYYYloeo/7Ki8ecw1wtufC4671BM1LL4uLi9HW1gYA\n6O3thSzL6tu23NxchMNhDAwM4P79+7h48SKKi4sjHkNE+uNci0dSonj/5nQ68dNPP0GSJDgcDly7\ndg1ZWVmoqKhAd3c3nE4nAOC5557D3r17Hzpm6dKl8Pv9j3WJlx4iXVZ25coVnDx5EikpKcjLy8Ox\nY8eQkqLPrylEc/nbiRMn8Msvv6ClpUWHhP8TKevNmzfx+uuvY2xsDKtXr8Z7772XkDlbW1tx/vx5\npKSkoLCwEG+//bZuOQGgr68Pr7zyCl566SXU1tZO2fc48yTKXE+ItMY2mw0Wi0U99eR0OmE2m/WK\nqorVWgMAlDj78ccflZdffllRFEW5fv268sILL0zZv3XrVuXPP/9UxsfHlerqaqW/vz/ekaallbOi\nokK5efOmoiiK8uqrryqXLl2a84yKop1TURSlv79fqaysVGpra+c63hRaWevr65ULFy4oiqIojY2N\nyo0bN+Y8o6JEznn79m1l48aNytjYmKIoirJnzx7l6tWruuRUFEX5+++/ldraWuWdd95RWlpaHto/\nV/M0X+Z6glbejRs3KuFwWI9ojxTrtY77S9NHXeIFYMolXikpKeolXnqIlBMA3G43LBYLgAdXIYyM\njCRkTgA4fvw4XnvtNT3iTREp67///ouff/4ZNpsNAOBwOLBkyZKEy5mWloa0tDTcuXMH9+/fxz//\n/IPs7GxdcgJAeno6zpw5A1mWH9o3l/M0X+Z6QjRzk2hivdZxL/tQKIScnBx1e+JyLQDTXuI1sW+u\nRcoJQD03GQgE0NHRgbKysjnPCGjndLvdWL9+PZYuXapHvCkiZR0eHsbChQvxwQcfoLq6GidOnNAr\nZsScCxYswP79+1FeXo6NGzfi6aefRl5enl5RYTAYkJGRMe2+uZyn+TLXE7TmBnjwgqO6uhpOp3NG\nVyfFWqzXes5POifCkxiN6XIODQ2hrq4ODodjyn84epqc86+//oLb7caePXt0TPRok7MqioLBwUHs\n2rULZ8+exbVr13Dp0iX9wk0yOWc4HMann36K77//Hu3t7ejp6cHvv/+uY7rENF/mesJ/89bX1+Ot\nt95CS0sL+vv71f8RnUziXvYzucRLD5FyAg+Gft++fTh06BBKSkr0iAggcs4rV65geHgYNTU1OHDg\nAHp7e9HU1KRX1IhZc3JysGTJEixfvhypqanYsGED+vv7Ey6n1+vFsmXLYDQakZ6ejnXr1sHj8eiS\nU8tcztN8mesJWvO9Y8cOLFq0CAaDAaWlpejr69MjZtRm8hzHvexncomXHrQuKzt+/Dh2796N0tJS\nXfJNiJRzy5Yt+O677/Dll1/i448/htVqhd1uT8isBoMBy5Ytwx9//KHu1+v0SKScS5cuhdfrxd27\ndwEAHo8HK1as0CWnlrmcp/ky1xMi5b19+zb27t2Le/fuAQC6u7tRUFCgW9ZozOQ5jurSy9maySVe\nenhUzpKSEjzzzDNYs2aN+tjt27ejsrIyoXJWVFSojxkYGFDfluopUlafz4c333wTiqJg5cqVaGxs\n1O1y1kg5v/jiC7jdbqSmpmLNmjV44403dMkIPPhh8+GHH+LGjRswGAwwm82w2WzIzc2d83maL3M9\nIVLezz77DOfOncOCBQuwevVqHDlyBJIk6Zo31ms9J2VPRET64s1LiIgEwLInIhIAy56ISAAseyIi\nAbDsiYgEwLInIhIAy56ISAAseyIiAfwfQ96po68cdJkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e1fcc8908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(grid[0, 0])\n",
    "plt.subplot(grid[0, 1:])\n",
    "plt.subplot(grid[1, :2])\n",
    "plt.subplot(grid[1, 2]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This type of flexible grid alignment has a wide range of uses.\n",
    "I most often use it when creating multi-axes histogram plots like the ones shown here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HcvnyZTKZDOfOnaNcLtPV1YXNZsNoNFIoFITgl8tlnnzySebn58U9VCoVlpeX\nCQQCLC0t0dDQgMFgoFgsks1m8Xq96HQ6enp6MBgM2Gw2zGYzJ0+exOPxMD09jdFoxOVyCUOl9ZOE\n1WpVfIKQzn+9t4nkETIzM0N/fz+Li4ukUilcLteWC7iPKrJIy8jcBw8yoLJZK540Hffaa69x6tQp\nkdJoaGggGAyyb98+qtUqv//97wmFQnz88ccUi0UOHTpEPp8X+WApQpSM910uF7FYDICZmRl8Ph8f\nfPABfr+f+fl5yuUyJpOJUqmEXq8Xwy+lUkncX0dHB5VKhZWVFWZnZ8WiWSlPLS2rtdvtTE1N0dTU\nRCaToVAo0NLSwrPPPsv7779PsVjEaDSKIZbZ2VlOnjzJ/Py8eNDE43GUSiXZbJbR0VGxhcViseB2\nuxkcHCSVStHS0iImCVc/9CqVCkNDQ5RKJfFQKBQKa34GO32QBWSRlpG5Lx5kQGWjSG51NO5yuXj1\n1VdJJpMsLy/zk5/8BJVKRT6fZ//+/aTTaUKhELlcjmKxyOeff057e7voVY5Go6RSKSwWCwaDgeXl\nZeGl3NjYiM/nI5vNsnfvXkqlkmjdUygUwpJUpVKRy+VQKBTC9Og73/kOU1NTBAIB0X0SCAQoFAoc\nOHCAW7duYTabsdvtdHd3s2fPHuGZkc1mMZlMOJ1OGhoahEhKrYN6vZ7p6WlaWlpQq9XC76NSqXDq\n1CmxDPfYsWM8//zzd0XBqx96UndHPp9Hr9djNptpbW0Vn0p2C7JIy8jcBw+6gXq9V/RG0Xg6nebd\nd99lZmaGmpoazp07x9jYGDU1NXR1dWEwGMjn82g0GiFOTqeTl156id///vfC1rOzs5N8Pi/6lIvF\novDYUCgUNDc3o9VqKZfLLCwskM/nqampEaJ+6NAh1Go1CwsLBINBamtruX79OqVSCZvNRqlU4uc/\n/zkA8/PzHD16lEKhgM1mI5vN4vP56O/vx2AwsHfvXrLZrJgGNJvNmEwmsf3FYrFw+vRplEolfr+f\np556CpfLteYhuP6M1z/0Vm8eVygUtLe378rJw10j0nKBUOZ+eZBJtAfNb25W6JqYmGBkZIT29nbe\nf/995ubmSCQShEIhisUibW1tLC8vY7fbKRaLwjvDYDCgVqupq6sjmUzicDgoFApEo1EuXryIRqPB\n6/Xi8XhobGzkwIEDRKNRhoaGOH/+PKlUioWFBbLZrEgtHDx4EKPRSEdHB7W1tbS0tAAQiUT4+OOP\nUSgUXLk14/OOAAAgAElEQVRyhWPHjuH1enG5XJw8eZJAIIDP52Pv3r0MDg4yOzvL0tISmUyGpaUl\n/H4/1WqV2dlZ4M5uRoA/+ZM/4Z133kGtVlNfX49CoRCplNVRstQBks/nOX78uMgzrz576cEp3f9u\nE2jYRSItI3M/PKj5EWwtvym1oa0esBgaGqJYLPLhhx8yOzvLzMwMDQ0NjI+PC3Mlj8cjlr9KXsvJ\nZJJsNktzczNqtZonn3ySSCRCpVIhnU7j9/vJ5/PYbDYqlQrd3d0AvP/++xQKBVHA02q1aLVaIfqS\n2b/T6aRSqRAOh8nn81itVn77298SDAaJRqO88sorBINBenp6mJiYYG5ujmKxSCwWo1AoMD4+Lj5d\nKBQKBgcHicfjtLW14XQ6GR4eRq/Xs2/fPsLhsIh+5+fnxYMIEJ8apAea9N6i0SgAr7766n2tF9tt\nyCIts6vZrE/5qzSBlx4Ekn/Gc889J5bC9vf3s7CwQKFQEL3EPp8PvV5PJBJhYGAArVYr7EBv376N\ny+Vifn4ep9Mphj6MRiN6vV70BlerVSqVivDpMJlMLCws4Ha7xSRhMBikrq4OvV6P3+9HqVSSyWTE\nqqxLly5hNpt5++23USgUqNVqFAoFw8PDNDQ0MDQ0JEaxlUolBoNBrM+6evUqJtOdzeV9fX0YDAaC\nwSDvvPOO6M1WKpXo9Xp6enpwOBw888wzzM/PEw6HOX36NGq1mtdff138PPL5PNFoVIi41AmzevvM\nbigMfhGySMvsWu7Vp/xFueWNlqVutPx0o69LD4L1S2El8/pyucyvf/1rstksGo0Gk8lEfX09hUKB\nQCBAJpMhGo3S2dnJ0tISuVwOm83GH/3RH7F3716SySQ+n49//Md/FMW4V155haamJoxGI36/X3hm\nFAoF7HY7Q0NDJBIJEokEKpWK2tpasazV5/ORz+cJBAJigCaZTFKtVslms/T29gqHPK1Wy7PPPkt9\nfT2hUIjJyUkxDVlfX08kEmFiYgKHwwFALpcjk8lgNpu5cuUKTz31FBMTEwwNDVGpVJibm+ODDz6g\nvb2dmZmZNab+/f39optlenqaarXKO++8Q29v710WpbsZWaRldi336lO+10fkjVzZpJTF6mLfZmkT\nacPK/Pw8TU1NonA2NTWFxWIhFothMBioqakhkUhQKpVEWmNsbIyenh5aW1s5ceIE+/fv52c/+xm1\ntbVMT0/jcDgwm82cOnVKPGgymQzT09OEQiEikQjVapV8Pk8+nyedTuPxeNDpdPT19dHY2MgvfvEL\nbt68KXxA8vm8sByVDPYrlYpIn0iCfPXqVbLZLAqFgtraWtrb24Vf9fz8PIlEgtbWVhKJBH19fWJY\nZ2FhgZqaGrFoVpqY9Hg8KJVKqtUqV65cEQ/N1Wdrt9txu93odDqMRiOTk5MYjUYqlcqOnSDcKrJI\ny+xa7tWnfK9f7pmZGSYmJuju7mZlZUU4zq1Oj6x/AKx3YJOWq0o51UuXLtHa2irWTbndbubn56lW\nq3R2dpLL5fB6vWi1WjQaDQaDAbPZzOnTp1lYWGBxcZGLFy/i9/vFsIk06qxQKFAoFMI7WhoZV6vV\nmEwmvv/974uHkpRyUSgUjI+PU6lURK91oVAgn8+LiUBJ7CcmJkT0rdPpqK+vx+l0kslksNvt1NfX\no9fr8Xq9WCwWEokEY2NjGI1G8vm8EGK1Wk02mxUPFmmxbGtrK2fOnEGr1XLx4kUOHjy45mydTifx\neJxMJoNKpRKmTjt1gnCr7CiRljs4ZLbCgxSWQqEQv/jFL5ienubixYt885vfxG633yX0qx8A6x3Y\nvF4vExMT5PN5Ll26RCAQYGFhgf379xOPx9HpdLz88suMjo7i9Xqpr6/n6tWr6HQ6IpEIOp2OXC7H\n7OwsPp+PQCBALpcjFAqh1WrJZrNrBK9YLBIIBIRIFwoFFAoFDodDGDlFo1Hcbjcul4vW1lacTict\nLS3cunWLQqFAoVCgpqaGUqkklgB0dHRQV1dHf38/wWCQGzduYDKZUCqVRKNRisUi3d3dlEolmpqa\niMfjTE5OCtMns9lMZ2cnx48fZ3JyErfbjdfrJRAIrNkyIz1YisUiV65cobW1VWyYkXw4pGGW1aPj\nj0MUDTtMpGVktspWC0uBQACFQsGzzz7L7du32bNnD/39/Wvy05Izm/QAyGQyjI+Pi8hP2u0Xi8UI\nBoNUq1WCwSC3b9+mpaWFAwcO8Mtf/hK73U4oFEKtVjM0NCS2rmg0GhYWFrh27RoTExMsLi5SKpVI\nJBJcuXKFQqHAwYMHhaCm02nUajWFQkH0SCeTSaLRKLlcjvHxcbGaSqvVMjAwgN1uFx0h5XKZWCyG\n2WxGoVBQX18vep/T6TRXrlyhpqYGp9NJuVwmlUrR0dEhtpRLVqVSu14kEiGZTNLd3U0wGESlUnHh\nwgUaGhq4ePEig4ODdHd34/P5+Pzzz4VgS2u6bt68SU1NDbFYTGxreRwKhJshi7SMzCo8Hg9qtRq/\n34/VahVjzSaTacPdeh6Ph3Q6LdZcKZVK3G43SqWSWCxGKpUS6Qibzcb3vvc9ksmkmPQbGRkRhTap\na6Kurg6VSsXi4qLwmi4UCqLwmEql0Gg05PN5/H4/uVwOo9GISqUSuVvp76XUTD6fZ3l5WRQtn376\naSYnJ9FqtYTDYTo7O4XBU01NDalUikAgwBNPPCHy3W+99RZOpxOPx0OhUMDr9YrpvtHRUZLJJMeO\nHePGjRtYrVa6u7sxGAyUSiUaGhrYs2cPt2/fJh6PEw6Hicfj+P1+Ydrf2dkphmbMZjNjY2NoNBqm\np6fp7+/fsVajXxZZpGVkVuFyuXj99dcJBAJ4PB7RabB6t57ZbMZmswkj+Y0m4fbt24fRaGRkZAS7\n3Y5Wq8Xj8WAwGABIJBKi9S6TyeD3+/F6vfT29lJTU0OlUqFQKJBMJikUCmSzWeGxLL1GbW2t6M5I\nJBKi/a6xsRGVSkUoFKJarYqe6HK5TKlUYmRkhGw2S6lUIplMAnfa3V544QWOHDki9iP+93//NxMT\nE6JHW6vVolQqWVpaQqFQYDQamZmZ4caNG4yNjTE5OYlCoaCpqYlCoYDP5xMOfrdv3xZ7EF966SUM\nBgORSISFhQUsFgsDAwMcPnyYUCjE7du3mZiYQKfT4XK5uHDhAslkEqfT+dh0dKxGFmkZmXVI48kS\n6XSaqakpYR5//fp1PB4PdXV1IrrbaBIuGAyi1+vJ5XK4XC4aGhrIZrOcP39epAvUajXlchmbzYbd\nbqdQKFCtVgEol8ui/1mv16PVajEajTQ2NhKLxVCpVMzMzJDJZAAwGAzU1tbicrmYm5sjl8tRrVZx\nOBwitaLRaERBUCrGuVwuVCoVDoeD1tZWbt68ya1bt4Qhfy6XE73YgBD8s2fPsri4KNI0HR0dRCIR\nhoaGyGQy2Gw2mpub1zz4LBYL1WqVTCaDyWSioaGBarVKa2srzc3NZLNZnnvuOUZGRigWiywsLIj1\nXDvZE/rL8EiKtFwglHlYrO99Xr1JZWZmhtraWjweDydOnKBcLm+64qm9vZ3PP/+cY8eO4fP5+MM/\n/EOee+45Tp06xejoKKFQSAxrFAoFYSGaSCSE8ZBSqcTj8aBQKMjn8+RyOdEjncvlKJVKaDQa7HY7\nqVRKuNsNDAxw+/ZtrFar6AZpbm4mFouJQqPUnx2Px5mbm8NisfDee+8xOzvLwsICo6OjrKysUF9f\nj91ux+Fw0NHRwdTUFBcvXiSfz6NSqXjxxReF857FYsFut3P9+nWy2SxjY2P09vZSV1fH888/T0dH\nBydPnmRkZET8++bmZvL5PAMDA7hcLiYnJ4lEIqysrNDW1kY+n6erq2vHe0J/GR5JkZaReRhs1Pu8\n3sSnvr4er9crtqvcSzSkgqLk2TwwMEC1WhWOdKVSiXQ6TbVaJZ1Oi2WzMzMzIn0hdTeUSiUWFhYA\nWFlZEUb/BoNBtKWZTCZcLheRSASTybSmZ3ppaQlA9CZ/61vf4ty5cygUCkKhEF6vl7GxMa5fv47Z\nbCYYDIqdiI2NjZw4cQKDwSB6pXO5HLlcjrNnz9LU1MQzzzzDvn37iEQi/PKXv6RUKnHmzBnK5TIO\nh0MUGJPJpFiMK+XWs9ksH3zwAd/5znc4duyY+NQi5aq7u7vXtNxttFJrNyOLtIzM/2ej4ZfVrXZG\no5H+/v413R6bDcNMT0+TTqdZWFigpaUFn88n8rJwZ0TabDaL3HAsFuPMmTO43W4UCgVWq5Unn3yS\nZDJJY2MjCoWCd955h0wmQy6XQ6PRUFdXR2dnJ1arlRs3bnDp0iWxnWXPnj2USiWq1aooYErRuMFg\noKmpiVgsRjqdJhaLidY/uLO9O5vN0tbWhk6n48iRI7S2tnLp0iVWVlaEq55CoSCZTBIIBJicnBR5\neIVCwY0bNwiFQly7do3Dhw+L95xOp7l+/brIuWu1WlKpFKlUivfee48//uM/pqOjQ3SjSIVY6b5W\ntzo+LvlpWaRlZP4/Gw2/SEVBacvJ/SCJ/dDQEDdu3BB+0FNTU7S1tVFbW8vQ0BA6nY58Ps/8/LxY\nqVUsFqlWq6INz+v1ihVXUtRttVppbm7mD/7gD6ivrwcQ+wSlVrxsNity2tIng2q1SjQaxWq1cubM\nGQYGBojFYmIbuWSYJKVRcrkcHo+Hl19+GZ1ORywW48KFCygUCsrlMlarFa1Wi06n4+OPP8ZsNtPQ\n0IDdbsdqtdLb2yuW05rNZs6dO0c6ncZut/P0008Le9OFhQXR6rd3716am5vF2LgUPW/khfK45Kdl\nkZbZ9WzmsbH+66u7NJRKpeh8AMT+v3g8jkKhoFQqoVarefnll+9a+6RUKjlz5gw+nw+lUonJZGLf\nvn00NTUBiI6LRCLBN77xDd59912R387n85jNZqxWKzdv3sTn82G1WmlpaRERvjTufe7cOVQqFcVi\nkQsXLohlrNI4dyKREPcjpUWKxSIGg0EY/N+8eZOVlRXK5TI6nU48OBwOh7BDPXv2LO3t7SwvL4v0\nR6lUwuVyiTOsVqtMTU1ht9t57rnnCIVC+Hw+qtUqbrebUCjE8PAwOp1O9JY3NDTQ1tbGlStXRDve\nT3/6U5577rk13hyBQGBTL5THAVmkZXY19zLa3+jrkoiv/rvOzk5hmzkxMcH8/Dy1tbUUi0VyuRwv\nvPDCmu3U4+PjXLhwQSx/jcfjokj24osvUq1WOX36NJOTkxgMBl544QWuXr2KSqUiFothsVjEQIjB\nYGBxcRGLxUKpVEKlUqHVamloaGBsbAybzUY4HGZhYQGlUkmlUgHuGBtJUa5CocDtdgu/6lQqJcyT\nJH/rWCxGLpejtrYWk8lEtVplZWWFRCIh8tDNzc2YzWbhOXL06FGy2Sw3b94UPdvXr1/n+PHj/Nmf\n/RmffPIJTU1NpFIpIpGIWG1VX1/P3r17xSRja2sr0WhUrPFa780hfcKJRCI0NDQwODgo+tcfBx6K\nSMvdGzJfF5tZk97LsjQYDLKwsIDD4RBtZ1InhtVqpVqtUiwW0el0om9aiurK5TLnzp0jm82KLSh6\nvZ75+XmuX79OIpHAbDYzPj5OMBikWCzidDrZu3cv586dw2q1YrfbWVxcFNu06+rqMBqNeDweMdhy\n8+ZNEokECwsLhMNhEomEWOiaTCbF5KG0IGD//v0sLi6K+ymXy9TW1gqXukQigdPpRKVS4XK5sFqt\nRKNRstks0WiUZDJJZ2cnLS0trKys4HA4aGlpwWKxiElHl8tFW1sblUoFl8uF2Wzm7Nmzwve6XC6T\nSCTW7D50u9089dRTXLlyBYVCIexXVxcKTSYTQ0NDYr2W3++nra3tIfzf9HCQI2mZXc1mJkubfT2d\nTnPx4kU++eQTqtUq7e3tHD16lOPHj4trlkol0YNsMploaWlhfn6eeDwuCmtwZ2ClWq2SSCRIp9Oc\nOXOGt956S3SGqFQqDh06BMBnn30m0iN2u53Gxkai0Sh+v594PC4WA4RCIQBhKbo6VSFtAZcWyep0\nOmHqLz1kMpmMMPnPZDIcPXpUtOFJKRJpScCNGzdE2qe+vh6Hw8Hhw4eZmZmhXC5TqVR48sknOXz4\nMOfPnxcLdaXdg5lMhqmpKfbt28fU1BQtLS20t7cLlz3pfbzyyiscPHhQnJvU0bI6Uq5UKlit1q/U\nB/xRRRZpmV2JlCtVKpXC/2H1L/5m5kvJZJJSqcTAwAC5XI6GhgZhqyktjT1+/DipVEpYhKZSKdRq\nNVNTUwSDQQwGgyhulUolLl68SCwWY3l5mXQ6jV6vx2QyiZHxVCqFXq9HpVJRLpfF0EkwGBSdHH6/\nH5PJRKVSIZfLkUgk0Gg0oqAodVsAwhFPpVKJiDQWi9Hc3MytW7dYXFxkZWWFTz/9FIvFglKpJBAI\nkE6nyeVy1NTUiLa8VCpFOp0G7qSApqamuHnzJm1tbfj9fgDa2tpwuVzMzMyI5bnlchmv14terxd9\n4JIb3noHOymiXp1iWr9M9kF3TO4GZJGW2dFsVBRcPYBy8+ZNYRK//hd/I9MeyR9jdnYWhUKByWRa\nY6sp5bMrlQput5u2tjZRCCuXy4yPj3P79m1MJhM2mw2VSkWpVEKn04mP/JLwabVapqencTqdLC0t\nsbKyglqtJhaLUS6XyefzAGKHoST0kiBrNBrU6ju/wpLlKECxWBTReiQS4dChQ4TDYWKxmChaKpVK\nQqEQHo9H5MOlr0ejUbRarRDipqYmfD4fOp0Oi8UivKF9Ph9nz54Vm8b/93//F71ej0ajobe3F61W\nS1NTk/j56PV6bDabGKVfzRdtzHlcVmVthCzSMjuWzYp/0i+80WhcI1aS18YX0d/fj8fjIZPJEAqF\nhEvbsWPHhIdHtVpFoVBw/PhxLBaLGHP2eDyEw2Geeuopbty4IdIRRqORUqlEY2MjHR0drKysiMKe\n1GombQGXImjJK8NgMKBUKlGr1WIvYaVSoVQqiTFyaZJQeihI3hpSsVEyWDIajcKKNJfLkU6nRe+1\n9B4aGxtpa2vjm9/8Jm+++abo6ujr6yMWi+FwOETnyeTkJL/61a+wWCzMz8+zd+9eNBoNFouFhoYG\nYdsqeXCo1WoSicRdPc73Eyk/rk54D0Wk//7v/37Dr8sFRZmtbPLeLPqSfuEzmQyVSoWrV6+i0Wgw\nm80i5XE/EbjH42Fubo4nnnhC2I2ePn2aa9euEY/HMRqNTE1NUVdXh8vlolgsolAoqKur49y5c8Ad\nwb906RLVahWv1yuWrVYqFbEYQCqsKRQKkTNeXl4WG1PgTqQs5ZylAqFWq0WhUIgipjRSLmEymVCp\nVGIFl2TcVCwWqa2tRavVsrS0RDgcFhtovF4vPT09NDU1CRc6j8dDIpEQntUOh4NoNEpzczPVapV4\nPI7H40Gr1XLt2jVqa2splUo89dRT4ucRDocBNvXg2Kz98XEU5fXIkbTMI8NWN3nfa/OK9Avf2dnJ\n9evX14gDcF8RuMlkYm5uToxc63Q6xsfHCYVCjI6OYrPZuHbtGna7nYGBAWw2G06nk6effpr3338f\nhULB4uIihw4dIp/PCw+MmZkZbDYbWq1W9F0Hg0ERTT/xxBMkEgmuXbtGsVgU202kPK/RaBRpGGmT\n9p49e8QOQykqLhQKVCoVRkZG0Gq1xGIxIdzSCLp0BtKCWYfDgclkoq6uTniEmEwmzGYzqVSK9vZ2\nxsfHyefzLC4uCp9pi8UiWhVra2sJBoPMzMywd+9eBgcHAcQ17hUpb/azeZyRRVrmkWGrm7zvlaeU\nPhpbLBb8fv8acdjsdZRKpUg1lMtlMpkMLS0t7N27l5s3b3Lz5k1hKTo1NYXVaiWbzZJIJDh9+jRu\nt5ve3l7RDzw4OEgmk+Gb3/wmRqORxcVF3njjDYLBoDAYCofDIu0geXAEAgF6e3uJx+Mify0V3rRa\nLXAnipYKdG1tbZjNZl588UWKxSI+n49PP/2U5eVlkQZRKpWiDzkej3P16lW0Wi1Wq1WkUKTx9Kmp\nKd544w0OHTpEpVLB5/MxPz+PTqfj5s2bFItFYazU39/P/v37hbPdpUuXmJqaoqamRvh7SJ9epO0q\n6z+9fN3b3HcaskjLPDI8SAV/dTFvI+OdzYR8/euk02k+/vhjhoeHKRaLdHV10dfXh8PhIJVKUalU\naGhoYHFxUTjNqdVqQqEQpVIJi8VCX18fdrudZDJJMBjkgw8+QKlUotfrWVpaIp1OMz8/LwRbrVaL\nVjRApC2USqWI2JPJpPCJNhgMhEIhUTi02+1iS7e067Curo5cLodarUapVJJKpSgWi+LPgOgoKRaL\n2Gw2enp66OrqIp1Oi/udm5tjeHiYmpoasV0lk8kID+rFxUWsVitPP/30molLl8vFqVOnxJ+PHz++\npqNm/cTnvba5FwqFNUXbxxVZpGUeGR60gv9FaZL14rDR60xPT3PlyhVisZjweXa5XBw+fJhbt24R\nj8e5cuUKfr8fp9NJXV0d7e3t9PT0cOHCBSYnJ1leXhapgb179xIIBMRy10gkAiC2eEuj5eVymXK5\njNlsxmg0olar+fDDD1EqlWIgRqFQYDabhYNepVJhamqKnp4eCoUC09PT5HI53n77baxWKz09PeJa\nkmmSVGiMxWKiT1maEty7dy9er5e6ujreeecdYXxkNBpZWlpiaWlJjHe3traK7o1qtUoqlbpLpKVW\nxS/6Gd5rm7tkprS6aPu4CrUs0jKPFA9Swf+ij8gbFQk3eh2tViuGPKxWKwC/+93vuHLlCvPz82Ib\ntsvloqamBrPZDMCtW7dIp9MEg0G6urrw+XxMTU2JST6FQsFnn30mCoMGg0GYGknrpex2u4h4V49q\nVyoVVCoV3d3dLC4uEolEUKvVlEolstkshUIBjUaDQqEgm80Si8XE5m5pge7t27eFI540QSn1ZavV\nahQKBSsrK7hcLtxuN1NTU2g0GgKBAJlMhkwmg06no1qt0t/fz+TkJGfPnqW2thaHw3HX4Mn9/Ayl\nrpJisbhhTUFaAXa/aY+tFJx3GvJYuMyO515pknQ6zcmTJ8Uv8PPPP7/hL7Hb7ebgwYMiomxtbWV+\nfp7R0VHh1BYIBGhoaBA7APV6PT/+8Y+5ePEiarUalUpFoVDA5XIxNTWFw+EAELlmKWquVCrU19cL\ns6NYLIZarSaTyQibTslUCO5MOM7MzNDW1iYKhfl8XtinOp1OxsfHRepE6vCoqalheXlZbHdRKBRi\noCSfz4v7mZ+fZ3l5mU8//RS73c6RI0fEVvKzZ89is9nQaDQ8++yzNDc3c+HCBeHY5/f7mZmZEYZH\n9yOQqz/5AHR3d98l9FtJfW214LzTkCNpma+Eryqy2Swq3ixNEgwGRW51dnaW/v7+DXulTSYTzz//\nPMlkkuXlZT766COmp6eZmJggHo9TLBbFnkK73Y7RaCQUChEOhymXywQCAfR6Pe+++y42m42pqSlU\nKhXxeJxqtSqKkdlsFrhjLapWq0VKQ7p+KBQS5k1LS0sib61QKNi7d6/w7ZAi8Gw2y8LCAkajUWxr\nmZmZwWq10tDQgM1mQ6FQ4Pf7RRRdU1ODRqMR7XXZbJb29nYRlS8vL4u2QpvNRiwWo1gsUigUuHz5\nslgmMD09jVqtJhgMMjg4KH4OwF3bbe5VHJS6Vdb/PO439bXbi42ySMtsO182srmXtehm193sI3Ym\nkyGZTIrx5FAotOnWaelrH3/8MdPT0ySTSbxeL8lkEo1Gg9VqFaIkjUGXy2URWQ8MDBCJRPD7/cLw\nyGg0it2Bs7OzYqqvWCzi8XhYWVmhWq2yvLyM1+tFq9Vis9mA/9vAotVqaW5uZmVlBYvFInw6pHPw\ner2YzWYmJiaEUJdKJfx+v5hEdDgcYtGAlN+2WCx4vV7m5uaIRqNoNBqcTid79uzBaDTS29srRtnH\nxsZIJBLcunWLbDZLOp3GarVy7NgxUQytVCoEg0EmJyfFvQ0NDXHt2rVNi4P3ipK/qCgssdtHxmWR\nltl2vkxkcy8h3up1pQ0per2eQCCATqdjaWlpw4m31fcumeNPTk5isVg4fPgwdrudSqUipu2knYdt\nbW1YrVZUKhWLi4tiTRVAbW2tsAhtbm7G7/ezsrIiCocmk4loNCp2GGazWVwuF8lkkkQisWZVlzQS\nrtPpUKvVaLVaCoUCRqORdDrNM888Iwp50ooqlUpFT08Pfr8fjUbDysoKBoOBPXv2sLS0RHNzM1ar\nlf379xOPx4UXyMDAAKlUirq6Ovr6+jh9+rRw/IvFYtTX19PU1MT+/fvFe5ec64A1PyMpbbNRcfB+\nouT7eeDv9pFxWaRltp0vE9ncS4i3el0pAn7hhRe4ePEi1WoVp9NJKpUiGAwKo5/1uVDJAtRgMKDT\n6RgcHKSlpYVcLkd7ezv5fJ5UKoXNZmPPnj2cPHkSjUZDKpWivr5etI7V1dXR3d2N1Wqlra2N06dP\nrxke8Xq9YnOKVEysqalhbm5O9Ea7XC6i0aiYNpyenhZ5ZWnEWqvVEgwGeeqpp7h16xbDw8Oida+m\npkaIudVqRafTieLjSy+9hM1mY3h4mFwuh9FoJB6Pc/LkSYxGI3q9ntbWVjEWvnqJweHDh3nttdeo\nVCrs27dPCKRk4yoNwvT09Gw6cHQ/Ynq/D+bdPDK+ZZH+p3/6J65fv45CoeAHP/iBmCbaDLlIeP9s\n9WwfVb5MZHMvIV5/Xbj3UtLVZvGJRIJKpcInn3xCV1fXprvyTCYTTU1NmEwmmpubSaVSDA4O0tvb\nK0yJLBYLCwsLKBQK3n33XSYnJ9FoNCL/7HQ6aW5u5vDhwzidTurr67l9+zaVSkXkmGtqakQke/ny\nZYrFItFoFI/HI+5hbm5O+DoXCgXhqmcymcR+Qa1WSy6XY25uTiy3VSqVovUunU7T3t6O0Wgkn88T\ni8XE2Pnbb79Nd3c358+fx2q1Mjc3J6YQFxYWOHnyJO3t7ezZs4eGhgYUCgXpdBqDwYDdbhdnNjw8\nvJQRq3UAACAASURBVMbMqlgsig6ZiYkJsQrrQca9d3sq437YkkhfuHCBubk53nzzTaampvjBD37A\nm2+++VXd22PFbjvbB41svkjgV+cpJZ+NfD7P8ePH71pjJV1L2j4t5W1dLhfZbFaYAq02XpLy4TU1\nNYTDYTHUYjKZOHnyJJcvX2Z4eJhqtUpLS4vYfShF7W63mxMnTpBIJFCpVAwPD4ue45aWFtLpNJFI\nBLPZLNIfHo+HXC5HLBYjFAoxMzPDwsIC+XyesbExMTpuNBpFp4Zk3m8wGMjn88TjcZaXl0Uu2mAw\niN5mn8+H0+kURcRIJEIqlaJQKLC0tCQ2wEgWqoVCgfHxcXw+H/F4nIaGBnp7e6mvr2diYoKXXnpJ\nbPwG1ozSG41GlpeX0Wg0Ytu3ZCD1IHWK3Z7KuB+2JNJnz57lxIkTAHR0dIixValfVObBkc/2/7gf\ngU8mk6TTaeGNDPDqq69u2B/t8XiYnZ3lwoULAIRCIarVKleuXAEQxTSpR1qr1fLtb38bv9/PCy+8\ngMlkYmRkRIh9bW2t6NRwOBwUCgVu376NQqEgkUhw5swZlEqlKCCePn2alpYWDAYDGo1GFBeXl5fX\nLFstFouYTCa0Wq1YKLu8vCy6SiS/53g8DtyZHLRYLCKXHI/HKRQKwi5UqVSysLBAd3c3oVAIm83G\nM888Qy6XIxgMMj4+Ltz27HY79fX1jI2NMTk5KT4VGI1GzGYzBw8e5NKlS9TU1HDmzBmeeOIJMakp\n3Y+Um5b+n10d/Uo/L6PRKIq59yu4uzmVcT9sSaQjkQh9fX3iz7W1tWKhpcyXQz7bO9xv657FYiGf\nz7OyskJtbS16vX7NL/7/Y+/MY+M87zv/mfueIYdzkBzepyhSMi3Jtiw7PmTHcZ210WadjesWWOwi\nXRQo0KyBLooWi3SBIkFRoEXb3T8Waf7prjebtomTJnWyrhU7sqVatyiJonjfx8xwhjPk3DOcmf1D\neJ4MKVISdVnH8/knsTmc9513zN/7vL/n+/t+N284tbW1kUgkpNFSVVUV6+vrNDc3E4lEZDTT2toa\nFy5cIJPJoNVq0Wg0/OIXv+DMmTOMjIxIA/za2lqam5vZs2cPc3NzG5JGRJ6fGKcuFAqMjo5SXV2N\n2WyWjnXpdFpK4QwGA/l8Xo6li151LBaTAy2iXeJyuWSPemVlBbPZLB3/hIVqoVCQKSmlUol4PM7o\n6Cg9PT185Stf4ciRIxQKBVZXV6mqqsLtdjM9Pc3k5KR0tautrUWn08lVvOiZT01NMTMzQzgcZmxs\nTIbX/uZv/iYWi0XedDbL8K5cuSK/j0OHDt3N/4weKm5r41DoLhV3nkfx2u5Eumez2WSklYhtquxX\nbqXFFZuGWq2WtrY2wuEws7OzFAoFnE4nPp9PPv6vra1RKBQ4cuSIDAIQGuJ8Pi834xYWFnC73dTW\n1srBDuGaJ5JWhOIik8nIVa9QZ4gWxPr6Ona7nUgkIouaUHJkMhmWlpbk766vr8tUF7PZjMPhkCtf\n4d63srIi+9+igGu1Wk6fPi0VGWJFa7FYmJmZkSt/h8NBNpslk8ng8XgIhULyKeHSpUusra2RTCal\nNFC0NSwWC7W1tRu+IyGfS6fT9PT0YLVaN8Rnbf7+H+W2xnbsqEj7fD7pQQBXHxu9Xu8dP6lHEXVt\ndy6xu55PxGajHoDOzk5mZmYA5Iq4XC5jsVjka3U6HQaDAavVikajkYZHwWCQ8fFx6f8sBj9Ez1an\n08k+tlA4WCwWjEaj9IUWcVZCaheNRuW4dzwelyvg6upqaaBkMplkknYgECCRSEhz/1QqRT6fJxgM\nYrFYqKqqYm1tjUwmIw2VGhoaZNGNRCI4HA7Gx8eZnp4mk8kwNDQkNxWFhE+s2G02G+3t7eh0OhKJ\nBPv37+f06dMYDAbg6tSjKOCbb5LiGoubrjjnreKzNr/2YZwavB12VKSfeeYZ/vt//++89dZbXL58\nWSYCX4/tDP634lFWgtzKtX3YuFM7+WJF1t/fTzKZZHBwkJMnT3LixAmpl/Z6vRw4cECGyNbW1mI2\nm+nu7gbgzJkz2O123G43u3fvxu/3s7CwQDabJZfLkUqliMVislCLYRCPxyPHvGtqamR7Y3h4mHK5\njE6nw+128/jjj1MsFhkbG5Mr3lwuJwMARCETLQuxSdfU1MT6+jq5XG5DKrjobYsnhXK5LKcZxZj4\n0tIS6XSaaDSK2WwmGo0Si8VkfNbevXvxeDw0NDRI/5BQKITZbJaqkyeeeIKf/exnlMtlzp8/z1NP\nPUU8HufQoUPXFFVx0xUbtOL9t1opP+xTg7fDjor0vn376O3t5a233kKj0eyoACuuj7q2O9vJF4ZG\nm6V0sNE0vqOjg2KxyPT0NBMTEySTSTo7O0kmk0xMTMjiOTo6Snd3txw02b17N9FoVLYZ8vk8e/bs\nkTmB2WwWnU4nV+YajQaXy4XZbJYWoh0dHUxOTlIqlejo6NjQh06lUtKDOZ/Pk81mZQsgFovR1tZG\nsVikpaUFjUYj2yJiqMZkMpHNZuVmo2jZiAQXl8tFqVTi4MGDfPLJJ3KFLPyvg8GgvOZarVbGah04\ncIDe3l7+9V//VZowvfrqq3Lz7gtf+ALFYpH19XV++ctfSr9r4YZX2bJwOBwUCgWZvC7S0StvvuFw\nWMooH3Wp3XbsuCf9B3/wB3fjPBSoaws376B2/PhxlpeXmZ6e5vnnn5cDKqJoiD5pOp0mFAoRDoel\nT3I+n8disdDe3k4ikWDfvn0MDg4yNTVFPB5nfX0dr9fLhQsXgKs30GAwyMrKCg0NDej1es6fPy+t\nPwOBAOVymd7eXmn8f/ToUc6dO0cqlaK1tZVAIMDIyIgc8zYYDHLjU/h6uFwu8vm8TAA3GAzSON9q\nteJ2u/noo49IJpOsrq7icDjkCl74XXd1dZHP53G5XCwtLck2Sjablb10i8VCS0sLer1eKjM6Ojrk\nan1sbGyDfG98fJxcLsczzzyD3++noaGBhYUFGRYgAnO3aln09fWRSCTw+XwcP36cSCRCY2MjL730\nEqlUiu9+97usr6+j1+s3bDyqVfSvUBOHigcO8Wjc3NzM9PQ0s7Oz2O12BgcHKRaLXLhwQUZFTU5O\nYjKZZNp1U1MTTz/9NCsrK/j9fmnFKcyKRFtjbm4Ol8tFsVjk7NmzjI+Py/RvQMZdAVJzPDQ0RFVV\nFTMzMwwMDBCNRslms/LGUSqVaGpqIpPJ0NLSwsLCAl6vVxZ7oeoQ9qEGg4H6+npisRihUIhz584x\nPT0tLU09Hg9ms1nGVYVCIbq7u+nv70er1cpjiRDa6upqDAYDVVVV6PV6XnjhBQCZonLp0iWefPJJ\n0uk0er1erm49Hg+lUukav2ebzSY3PEXqyuaWhd/vx+v1Mj8/z9TUlJQzio3bdDrNrl27mJqaIpFI\n0Nra+nn9Z3Xfooq04r5lu7BY4UMsTI36+voAGBkZwe12k8vlCAaD1NTU4PV6Zeiq3+8Hrva+JyYm\nGB8fR6/X88QTT/DCCy/w4YcfEovFqKurA2B2dpbFxUVCoZDUrYtHfaFXFooGh8NBLBaTj/crKyty\n0AauKlBqampobm5mcXERm81GqVTCbrdjs9loaWkhnU5z5coVaaJUU1PDwsICmUyGWCzGxMTENb7Q\nPp8Pg8FAIBAgn88zNzfHysoK+/btY3l5mampKQYHB0kkErLgut1umpubCQaDPPHEE+zevZv/+T//\nJzMzMywtLdHf3y975uJ4m0e629rasNvtBINB2a+GaxNvRAtrcHCQ6elpaQ515swZTCYTCwsLaDSa\na5Qhil9xV4v0o7wRqLg9tnp0TqVSfPTRR5hMJmmEX5n+PT4+zuDgIBMTEzz22GMsLCxgNBopl8sY\njUbq6upkYKrP58PtdksdMsDMzAyjo6NyFSrCWgHm5+eJx+OYTCY0Go3ckKupqcFoNNLW1sann37K\nyMjIhqRvcf7nzp2jqqpKKniGh4elI5/VaqWlpYXR0VFqamqk0kIUchEeILyhzWYzdXV19PX10djY\nyPLyMmtra5w+fZqpqSlKpRLBYFCe/9raGjabDbPZTE9PD7W1tayursqnjtnZWeLxOG63W+qhPR4P\nTU1NfPnLX5abkpsHhYS7XTAYlGqMrfYUbDYbfX19sh0l9NZNTU1oNBp8Ph99fX3XTIwqrqJW0or7\nks2PzlNTUxw7doz5+Xnq6uoIBAIbfIhtNhv9/f1cuXIFvV7PwsICPp+PAwcOyNeJ3mdzczOTk5Nk\nMhnZDgmFQsRiMbxeL5FIhOXlZfr6+pienmZlZQWLxUI+n5epKNlsVmYGplIppqenWV9fZ21tjaWl\nJYxGIyaTiWKxKHXQ8Xicixcvyj5uLpeTPeojR47IzUSh6onH49jtdpaWlpicnJTnLwq30Cw3NjYS\nCAQYHh4mmUwSjUYZGhpiZGQEgNXVVZxOp9RGJ5NJ6eAnphZramrkSv3s2bMkEgmqqqr4+te/vuUK\nt/L7EUk07e3tGxwLxfci/lf4dWu1WgYGBlheXsbtdvPUU0+pHvR1UEVacV+yWed85swZ5ufnicVi\naDQaampqrlEAiJ5vsVgkFApRV1eH1WrF7/fz5S9/mampKc6ePcvY2Bg6nY66ujrK5TJNTU1ysGVl\nZYXV1VVaW1v58MMPcbvd6PV6qXQQBVckhTscDux2O0ajEbPZzNramlxFw68iuUQiS6lUIpvNYrFY\nKBaLcmWdTCax2WxkMhmam5vp7u5mbm4Og8GwIbC2VCpRKBTkaHcul8Pn89HQ0EB3dzfHjh1Dp9Nt\nCBIwGo3SdKlcLlNXV4fBYKC9vR232025XGb//v2yrxwMBuVmYzAYlKoN4VPi9/vl9zM3N8fQ0BDl\ncplgMLilf/RWnt+Puh/HTlBFWvG5cb0Js8pH53Q6zYULF6irq0Or1VJfX78hhVq8lxgYaWtro6am\nBo1Gw2effYbD4eDgwYMMDg4yMzOD2+2moaEBr9fL6uoqc3NzXL58GbPZLC1EGxsb5aP5rl27WF9f\n5/z581L21t7eTjablQW6s7NTFvl8Pi9HysWKWqxg4eoAjRiSEePt6+vrGAwGaVs6MTGB0WikWCzK\nARDxGp1Oh1arlavz4eFhOUW5tLREMpkkFovJ6wtX/UmefvppeS779+/nwIEDTE5OEo1GGRwcpK2t\nTUr5lpaW8Hq90vnvxIkTXLx4EY1Gw549e3jppZc2mFcJNc1W/tGPmrXonUYVacUdYacjvTdr5i76\nzTabjUAgQE1NzTWOd5U5hmIjMJ1O8+Mf/xin08no6Ch6vZ5yuUxNTQ0rKyt4PB78fj9+v5/Tp08z\nMzMjU8FFO2RqaoqhoSEcDgevvfYaxWIRrVYrTf9NJhP79+9ndXWVxsZG+vr6WFlZIZPJyA23eDxO\nVVWVVG2IMW/xv6Iwd3V1MTU1RTqdloM0+/fvx2az4Xa7cblcTE9Py03QYDAoU1jm5uaYm5vjN37j\nN+SgjtFoxGazSV+RdDrNwsIC+/bto7m5WbaBisUii4uLxONxVldXMZlM7N69WyozxsbGOHbsGOVy\nGafTCSDH3Gtra2Um4438oxW3jirSitvmVkZ6dzJhdr0hl1QqxeDgIJ999pks6C0tLdJzOZfLMTMz\nI6OqxCpbrMRTqRTRaJSFhQWp4AiFQtIKVIxyd3R0UF1dzU9/+lMKhQJarRaz2czCwoL0XBYyvHg8\nLkNWxTmIlgcgbxiVRay5uZmFhQU5cZjJZDh69KjsJbvdbl588UV6e3uZn59nZmaGiYkJGbGVzWZZ\nXl7G4XDIMXWx8nW5XNJfpKOjg0KhIFs+a2trZLNZOQjT0NCA0WiUmZDRaJRwOEypVKKqqgqj0UhL\nS8sGpUd/f7/c0K30j1atjDvDXS3SNzM1pxQgDz63MtK70wmzrR6Pw+EwH330EcFgkNOnTxMIBFhc\nXKSpqYlAIEBnZyeRSES61UUiEdra2mhvbweuppwMDg5Kve7a2pr8PC6XS3ppCC+Py5cvS81wPB5n\nZmaGhYUFLl26hEajoaGhAY/HI5NJALlxWFVVhdPpJJfL4fF4sFqtMtzV5XLJVW0kEiGTyWCxWNBq\ntXLzMJVK0dvby549exgaGqKxsZFYLCbd+nQ6HdFoVL5HT08PsVhMDseUSiUikQg/+MEPsNvtHDx4\nkLa2Nnp6eshkMvKatLS0yA3Uy5cvc+XKFelFffDgQcxm8zWeMqVSCZfLJb//Uqmk5HR3ELWSVtw2\n1yu427VBbtfMXcjxROJJdXU1DocDj8cjA12FfnpwcJBkMonVapXFSEwsTk1N0dXVJc1/RFCs2Wwm\nlUrhcDjw+Xz85Cc/YWZmhtXVVbkxmMlkaGxsRKfTcfToUbxeL3q9nrq6OjKZDE6nE71eTywWk0qQ\nnp4efv3Xfx2dTkc4HObTTz9l3759zM3NSVMknU4n1SPC37qqqorPPvuMwcFBJicnpemSWNUXi0WW\nl5epqqqSo+2NjY288sornDlzBrfbLQMHYrEYc3Nz2Gw2VldX5aSjxWIhFotx5coVNBoNXV1dlMtl\n6uvriUajnDt3jmg0isPhYP/+/bz00kvYbDY10n2XUUVacdtsV3Bv1Aa5nc0jERir1+uJRCJ4PB46\nOzvxeDyUy2WZM2i1WtmzZ4/sodpsNoLBIKlUCrvdTqFQYGJigoaGBnK5HOFwGLg6lGE2m/H5fORy\nOaanp1ldXSUUCmE0Gunu7mZ8fFy2G3w+n/SnWF9fp7Ozk7q6OiwWC5FIhIWFBSYmJpifn+fixYu8\n+uqr+P1+UqkUGo1GHlekrYhNS9EqWV1d5fz589TW1kodtE6nk62QSmtbg8FAb28vBoOBTCZDa2sr\nfr+f8+fPy5SYuro66Z3d2NiI3++XG6IajYa6ujrZ4kmlUszNzTE8PEw+nycQCBCNRuUT041uuMqC\n9PZQRVpxR9iq4N5NZzOtVsvU1BRra2ssLCxw6NAhTCYTX/3qV7FYLJw+fZof//jH5PN5isWi1PD2\n9vaSTqe5ePGiHAV/+umnmZubY3BwkEKhQC6XY2JiAqfTKW05xdi2+J0LFy7g9Xpl6yKbzaLX6+nu\n7mZ1dZUDBw4wPDwsC9zc3Jwc+z527BiJREKu/t9//315rSwWi7QKzefz6HQ6TCaT9JGenp6WY+M+\nn0/mHgrJ3dTUFHq9nrNnz/LGG28wNDQk/aOF6ZLZbKa1tZXvfOc7Mgzg9ddfx+PxyNVwKBSS3hwi\nhDedTm/wA9mcP7nVd6ssSG8fVaQVd427+RhcKpXo6ekhm81SLpdJpVLSlP7JJ59kfX0dp9NJNBpl\nbGxMFvVTp05JVzvRjpienmb//v0MDAxQVVXF7Oys1DuPjIzw6quvYrVamZqaYmlpSbY7hGn+nj17\nqK6upqGhQUrfxCi23++nubmZK1eukMvlGB4exuVyUV1dTTwelz1jsUHo8/mYnp7eMDxjNBqJx+MY\njUYcDgd+v1/qxYXXRW1tLZcuXZKRVhMTE/zoRz/C7/dTX19PJBKhq6uLpqYm/H4/p06dIp/P09zc\nzPLyMnv37uXJJ5+UkseRkRF5c/V6vQQCAelWt2vXLl577bWbKrbKgvT2+dyLtPKbfnjZ/BgM10/3\n3gniPTQaDVqtVpojwVXPjWg0Ko3txSo0l8vJHqroL4vcwEAgQH19PWfPnpUSOaPRCMD6+jo1NTVY\nLBaGhoakrahQbIj+sBhwaWlpIRQKMTs7y+DgIKurq3JaUKvVYjAYWFxcJJvN4nA4ZOvAaDSyvr4O\nQDabBcDlcvHcc89x6tQptFqtdPhrb2/HZDKxtLQkPaeFYZTIfAyHwxQKBf7xH/+RhoYG+vv76e3t\nZffu3czOzmIwGK4J260csRc3VyFVFD1+MYp/s9+T6lffHp97kVY83GxO975Tj72VN4DHHnuMkydP\nks1mOXPmjJx+s9vt6HQ6WltbyefztLS0oNVqSSaTNDU1EY1GmZmZwWKxkM1mee2116ivr+eDDz6Q\nCdxra2ssLy9TX19PZ2cnTqdzQ1SVMDvK5/PS4lNMAhYKBblaLhQKcurQ5/NRV1dHsVjk4sWL0ohJ\nZAVWBsm6XC4KhQKtra0yYMDhcFAsFllaWmJ4eFj2piORiOwti+nBbDYrcxA1Gg3nzp2Tm4dvvfUW\n8/PzHDhwQEr/rtdjFu+5kx7z7W4QK1SRVtwj7tRj7+YCUTly/N5777G+vi4384RBvjBn2rt3L3BV\n2bG4uMjq6iqLi4tyc29ycpL29nba2tpYXV3FarXi8/kwGo0kk0nGx8dxOp3U1NRIA6Smpiaqqqow\nGAxy7FoU5cuXL5PP52VEFyDHyqurq1leXpbZiLlcTvo9Cy2zyWSiubmZvXv3ksvlWFxcxOl0UigU\nOHfunLzh2O12DAYDXq+XQ4cOkUql8Hq9XLp0SRZsrVbLzMwMTU1NdHZ2curUKZLJJA0NDQSDQaLR\n6Iab5+Yes7jJplIpLly4QFNTEx6PRyo8roeaLrw9VJFW3BNu97F3uyQW8cdfKpXk4/7S0hKZTEZu\nzJXLZTweD62trSQSCex2OzU1NTJMVnhyiF6skN0988wzzMzMSFvSkZERGaLa1NRES0sLuVxO9pOz\n2Swmk4m9e/eytLTE5cuXZeEW5v1iSCabzdLS0sKFCxdkRqCQAS4uLqLX68nlcszNzbFv3z5efPFF\nPvjgA+bm5tDpdHLzUBg0NTY2Eo1GmZ+fx+l0snv3bp5++ml+9KMf4XQ6MZlMvP7669L9brPF63Y3\nT3FTFI5+Wq2WyclJdDodS0tL9PX10dbWduf+Q1FcgyrSinvCTh97K1fMwJZJLJUFRbyn8N0IBAK4\n3W4OHDhAsViUwxWRSIRoNMr4+DgzMzNotVpyuZzsP4fDYdkXDofDeDwe7HY777//PpFIhFKphM1m\nw+PxyHzEpqYmTp06hUajYXV1Fa/Xy/nz5+WGpkajkcGu5XJZemJYLBa+9KUvcebMGWKxmDy2kBT6\n/X4ikQhra2u8++67rKysMDExISf/9u/fL1PN3W43gUCA5uZm9u3bJ3vtr776KuFwmKeffpre3l6A\nDd/B5v7zZo17ZZAswMrKirypiL654u6iirTinnGz0VibV8wip7AyiUUEmorfEcGzImi2qamJubk5\nLl26JH0vstks58+fZ3h4GL1eTygUoqmpiampKRoaGqSlqcvlIhKJyFXy5pWkCJidmJjgypUrXLp0\niXA4TFVVFYlEgng8LiOtisWi9OwQSg7R7ujp6WF0dBSn00kmk5Ha5UQiQTQaxel0yoTwzz77DL1e\nj8lkwuPxyCnF2tpanE4nLpdLjnjPz8+j1WpxOp14vV45WTk5Ocnhw4c3TANe7+a5uUXV1dUFIKcj\nRSLL9b5L1Yu+fR6oIr2dEkSpPh4OKrMLp6ameOGFF6RznE6n25DEIorD4OAgZ86cweVyYbPZ6Ozs\nlG0CIc/TarUyYWV2dlZ6OQuXumw2K1sjvb29nDx5coPznFhxiyLpcDikVthisciEkbW1Nek1bTAY\nsFgscvUsPDxcLhcGg4Guri5yuRyhUAifz0c+n2dlZQWdTseePXtwOp309vZSKpUYGBigUCiwurpK\noVAgkUhgNpvJZDI0NTURi8Wor69ncXERr9crh1l0Op105ltZWZGp4a+//rpcRYsiutUY9+YWlVB1\niA3E7YrvjVpTip3xQBVpxcPNVtmFlW51le2PUCjE6dOnOX/+PIuLi/T391NfX8/JkydlkvaePXv4\np3/6J4aGhmS+3uLiIkajUUZJaTQaNBoNpVKJtbU1fu3Xfg2bzcb8/LyMtxL+Grlcjvr6eindGxoa\nkrFUZrMZl8tFMplEp9NJb43a2lrm5+epqalhcnKStrY26YAHVwua2EDcu3cvIyMjJJNJZmZmCIfD\nBAIBisUiTqeTZDIppxuF+ZPBYJD99nw+j9vtxmw2s7S0hM1mo7q6WgYX2O12enp6pH1pZStD3Pgq\np0XF08lms6TrPRFtFxKs9NG3jirSivsGsXLbvGKuLA6VRUAMb9jtdubn5ykUCjQ1NclWRygUkukj\nAH19fRiNRtLptPSJFgXH7/fLMAHhSz03N8eLL75IsVhkbW0Nk8mE2+1mcXFRGus7nU5pYCSmBUXS\nSyaTwWQyyfy+cDgsFRoTExOkUik6OjqYnp7m2WefZXp6GqvVyuLiooz+isVidHR04HQ6qa2txWAw\nsLa2RjgcRqvVks/n6ezspKqqiqqqKjlIEwqFqK2txeVyUVdXJ10AhTZb3BAdDgdHjx4lkUjg9Xp5\n5plnAG5ZLrndjVbpo28dVaQV9w03s7kYCoWkJWehUGBlZQWXyyWNj4Qj3eTkJK2trUxNTdHW1obF\nYqFcLtPR0YHRaJSuePF4XPpzPP7443g8HjnJaDQaZZGfnJzE7XYzPj4uWx2itaHRaGSxX1tbk5tr\nOp1OGugnEgk56Sj607Ozs1gsFqxWKwMDA4TDYemOl8lk0Ol0rK6uUiqVsFqtVFdXy+BaceOIRCJY\nLBba2to4fPgwU1NTfPDBB9KHWpyfcOH70pe+JK+raIfAVatUseIFblkueaMbrWLnqCKtuC+4UX9U\nvGZwcJCRkRFmZ2elR3JDQwPvv/8+uVyOeDxOb28vPT09NDU1yQK2Z88ePv30U1k0I5GIlMzt37+f\nlZUVnnjiCVlQRAvk1KlTnDx5Uq6cZ2Zm5OZedXU1er1eGv2LjUGj0SjTWYR1ZzweZ9euXdK61GQy\nUVdXh81mkwkxLpeLsbExGfklWjF1dXVSn71nzx5qamoYGBhgYWGBubk5EokEP/rRj3jnnXdkLuPo\n6CgzMzN0dnby9ttvS4WLiMISrQyhs04mkxvUHbcql1TDK3ceVaQVnztbTSMC1/yhi9Xo/v37peez\nMEMSgyCxWIyFhQV27drF3Nwcq6urZLNZrly5Qjgcpqamhvr6epxOJ52dnfzTP/2TPK5Ypfb3EwF9\n1wAAIABJREFU90vz/rGxMcbGxqTPciwWk8Mjfr+fbDaL2WzGZrORy+XIZDLU1tbKjMVYLEZzczPh\ncJhoNEp7ezs+n49SqYTH48FoNOJ2uxkbG5MOdXq9nkOHDjE5OYlWq2V0dJSmpibW19dZWlraEJ2V\nyWTYtWsXBoOBYDDI3r17efHFF2WAQUNDgwxBgF95cJtMJllQt9oIvJ1Cq4ZX7iyqSCs+dzZLvUKh\nEOPj49cU7XQ6LZ3kzGYz6+vr+P1+qaRYW1ujpaUFk8mEwWBgdnaWxcVF6Y/sdDoZHh5mfX0dk8lE\nOBymtbUVt9vN0tISg4ODtLa2MjY2xrlz5/j0008plUqEw2H0ej0WiwWXyyXHu10ul/TTmJqawmq1\nUi6XZeRWKpWiUCiQSqXw+Xzs2rWLw4cPy99JJpM4HA5isRjr6+ssLCzIgRuTyURDQwM2m41wOIxG\noyGRSFAqlcjn82QyGb74xS9y5MgR4KrFqXgCKZVKNDY2XpPkDUgPbpHzGAqFsFqtW/p930yhVTK7\nu48q0orPncrkaWGJuV3RBujo6KC+vp6hoSHMZjOTk5McOnSIM2fOAHDq1ClmZmYAqK+vx263s7S0\nxOLiIjMzM9KYqL6+nnw+z+TkJP/6r/9Kc3MzKysr2Gw2SqUSmUwGo9GI3W6nvr6e5uZmCoUC4XCY\n6upq1tfXaWhokJ7LDoeDhYUFisUi+XyecDgsDftDoRBms5mf/vSnPPbYY/T09LC2tsb8/DyJREJm\nA5rNZo4dOyajq7xeL06nE7vdTltbG2NjY5hMJkqlEgaDgTfffJPm5mZaW1tl7uN2Sd4dHR2YTCaZ\n8+hwOG5LJqdsSO8Nqkgr7go3s8KqfE1lTt7k5CSA7IkCUokwNjZGLBbD5/NhsViw2+20trbi8XgY\nGBhgbW1NTgRGIhEikQgmk0ma5dfV1TE/P8/8/DzFYhGTyUQsFpPa6SNHjlAsFmUyt9FopK2tjb17\n99Ld3c2RI0dIJBKsra3JfMRsNks+n2d+fl5OK4rYK51ORyqVIpVKEQwGpSmSw+GgpqaGXC5HbW2t\nNOIXPtdimEa0dcRgigiZtVqtG3rolYg2xuYkb/EzEejb19fH3NzcDTcIt/sulQ3pvUEVacUd52ZW\nWJtf09HRsSEnr6urSz6Gw9WhlaNHj0opXH19PePj49TW1jI5OUkwGJRTf8lkkrW1NQ4ePIjf72dg\nYACn0ykHWfR6Pdlslrm5OeLxOI2NjYTDYa5cucLKygpNTU0UCgX6+/tpaWmhp6eHvXv3Sp/mVCpF\nPB4nFothMpmIx+O0trYSjUalQkK0VITWOplMkk6n5dBMMpmkqqqK1dVV9Ho9fr+f6upqWlpaWFxc\nZHl5WW5WplIpnE4nfr+fvXv3btme2Mp4qr29nenpaUZGRuR04Ga9uTjWdhuE1/sulQ3pvUEVacUd\n53orrM2GPeI1wJbTbeL1bW1tJBIJfD4fx44d48yZM+TzeZqamjh9+jRjY2NygvDxxx/n2Wefxev1\n8umnn7KwsCAL5NraGk6nk1AohMfjkRtwDQ0N1NXVAZDJZKQCo1QqodFoGBwcZM+ePZjNZqlHzmaz\ncqpQxFxZrVYZfyVkeqlUSmYPiglG4fssjiGK/draGvX19fKz6HQ6qquricVihEIh7Ha7bGsIttt4\nDYVCcupSsLnXfKMNwut9l0rJcW94KIq0GBdX4+H3B9utsCqVBTqdDmBDUbbb7QSDQZlFKF5fLpfJ\n5XJYLBZSqZQ0J4pGowwNDbG4uEgqlQKubqA5nU5isRgffPAByWSS4eFh7HY7xWJRDobodDo5wFFT\nU8Pc3BwzMzM4nU7cbjddXV00NDQwPz/PyMiI7Id/5Stf4ac//SkWi4VQKEQsFsNgMFAul3n++edZ\nXV3l7NmzcjqwpqaG/v5+tFqt3MSzWq2USiWcTicOhwO9Xk84HGZmZkaqTJqamqS3tN/vJxgMMjw8\nzIkTJ66xB52ammJsbIyuri6KxaLs4Ytzf/nllykWi1u2I260QXij1bJSctx9Hooirbi/2GqFVZnu\nXVNTQyAQ2PDoDnDixAkSiQTT09McPHiQjz76iMHBQWKxGG63m/b2dlpaWshkMkSjUVmoXS6XbDH4\nfD7sdrsshCKdpFQqUV9fTywWY35+HrPZzNtvvy1Ng44ePcrw8DDT09PS5+PXf/3XGRkZQa/XEwgE\nyGazDA0NkUwmyWQytLW1sbS0RH9/P5cvX6a5uZnFxUU8Hg+pVEraj46PjzM5OUk8HpcJLiaTicbG\nRpqamrBarZw9e5aqqio0Gg1ut5t8Pi/juMTATSAQuGbEOhwO88Mf/pCJiQlOnz7Na6+9BiBbMsFg\nkF/84hccOnToltoRQpJYefNU3FtUkVbcFTavsISszO12EwqF0Ol0HDp0SD66T05OcvbsWQwGA6lU\nCp1ORyaTkfFUDQ0NmM1maaov3iORSGC1WqVvhViRZzIZtFotgUAAq9WK3W4nlUphMpmYmZnBarVy\n5MgRuru7KZfLLC8vMzs7S6lUIpfLMTU1xbvvvkt1dbVMUZmbm8Pj8VBXV0c2m5WJKPF4nM7OTp56\n6imuXLlCMBiUG5iVftdms1mqMhoaGjh8+LAcVJmfn2dhYYFkMrlhMxGQ0j/hhV1ZbIPBIBqNhuef\nf57R0VGam5vx+/3kcjlSqRSPPfYYVVVVsl0kvpubJZVKMTAwQLFYJBgMKgXH54Aq0op7glhRezwe\nFhYWcLlcDAwMyD/6dDrN9PQ0BoOBmZkZqqqqGB4eprq6mkKhgE6nY2RkhHK5zNramrTlXFpa4otf\n/CJWq5X+/n5cLpdcoVfGao2MjNDR0cGxY8fkKndqaoqf/vSndHZ2AqDX6ykWi3JTT0w/5vN5FhcX\nsVqtrK2tUS6Xcblc1NTUyKlDv9+P1+tldHSU9fV1rFYrJpNJ5h0mEgkZkdXY2Mgf/uEf4nK5uHjx\nIpcvX8Zut1NbW4tWq5Wbo06nk3379lFVVcX6+jparZaDBw9uKJK1tbXo9XoWFxdxuVy0trZis9k4\nfPgwcLX9I4z6RWL5Tka1lYLj80cVacU9o6Ojg+XlZen3XPlHb7VaaWlpIZvNkkqlqK6upqqqiu7u\nbsxmM8VikUwmw9TUFPPz83KluLq6yscff0xPT48cLgFkgfX5fExMTGAymaQ2WOQExuNx4vE4DocD\nj8fDoUOHiEQiXLx4kdbWVlKpFEajEZfLRWNjo1xxCznfuXPnSKfTpFIpamtruXz5Mj09PdTX19PW\n1kY6nZaxWPX19eh0OkwmEy+//DIDAwNEo1EmJyfl6txut2M2mzGbzezdu5fGxkY5si4GU4LB4DWx\nYW+88QbJZJK2tjb5ZOLz+Xj99dc3pH9vZaZ0o4KrFByfPw9Vkd5J8rhAbTbeXTZ7C4tV8eY/er/f\nz/79+4lGo+j1eqanp4nFYlI+duDAAX7+859z/vx5qqqqmJubQ6vV0t3dzfr6Om1tbVRVVREOhxkZ\nGWFwcJC2tjZpjD80NCTldaurq3g8HqLRKMvLy/zzP/8zgUCApqYmOjo6ZLGrNGm6cOGCLLpVVVV8\n/PHHcuAlEomwsrKC0+lk//79MjC2rq4Og8FAPB7HZrORzWaxWq10dHRw4cIFotEohUKBiYkJHA4H\nu3btIpfLsbKygtFopKGhgdbWVlZXV68ZTNnKrW67TUGRvrKVmdLN5BMqBcfny0NVpBWfL5u1utt5\nC1dqoCtXhAcPHmRychKXy8Xo6Cjd3d1kMhm5ku7u7gbA4/GwsrJCNBrlzJkz0n708uXLUoI3MDDA\n8ePHcTgcdHZ20tbWRi6Xo1wuk0wm8Xq92O12TCaTjNMS/hhvvvkm6+vrUukxPDws9dWrq6uEw2EZ\nPDs7OytzD0ulkvznYDBIPB6XSS7pdBqv1yvHyrVarRw0sVqt/MZv/Aa7d+8mmUwyNzdHKpWip6dH\nZi2KwRSv18vs7Kwc576ZVoQotELCt9lM6UYoBcfniyrSijvCVlrd65n4bzXccuLECc6cOcPo6Cjh\ncBi/3093dzdf/vKXmZubw2Aw4Ha70el0tLS0yExBoWk2mUyMjo7Kjbvu7m5isRjHjx9nfX0di8VC\nOp2W2YALCwuEw2Hy+TzFYpFsNks8Hud73/seu3bt4vjx45hMJpaXl7Hb7cRiMYrFIul0mkAggFar\nxWazyWQVv98v3e/8fj9Op5Nyucxjjz3G+++/j9vtpqqqiieffJIXX3yRTz75hGKxiMfj4YknnpDX\nRJzvwMAAX//61/H5fHIw5ejRowDY7XYOHjx4U60IcfPcPMyiCu+DgSrSijvCVhtMN/IWrlx5JxIJ\n6XJnNBqpra2lsbGR6upq3n33Xc6fPy/VFf39/TQ1NbG4uEhLSwvRaJRYLCajqaqrqwkGgxw9elRG\nVz377LNy1Lurq4uRkRF6enqIx+MsLi4yOjoqR8hF4OuVK1ekRlm0R/r6+qirq+OVV16RvXHhDRII\nBMjlchQKBdLpNA6HA5fLRalUYteuXezfv1+uun0+H2+++eY1BTMYDMr2zdTUFMFgEJ/Ph81mo6+v\nj0QiIdsVpVLphvK4rW6e21nBKu5PdlSkT5w4wV/+5V+i1WppbW3lW9/6ltyoUdweD/q13WqD6Xr9\nzM3Fo7+/H4fDIVNOhNlQLpcjEolQLpdl1FMwGOS1114jnU5TLpcpFAq88cYblMtl/t//+3+sra1R\nLBYpl8v4/X6i0SiffPIJzz33HOVymZGREXQ6HYFAQE4hvvbaa4yPj9PY2Mjly5dZX1+XEkCdTse+\nffuYmJjA6/Wyvr7O7OwsXq+XtrY2aVUq5INut5ve3l5eeuklfD4fyWSSmpoaSqUSk5OTmEwm2VcW\nBVPcsIT0cHR0VDrbiZ/Z7Xa8Xq9sV2QyGRkXtp08TqkzHnx2VKS/+c1v8r/+1/+itraW3//93+fT\nTz/l+eefv1vn9kjxoF/byoKs1Wo3aHJvZty4VCrx0ksv0dfXRzqdlq87d+4cZ86cYXl5WYbHWq1W\nOjs7eeaZZ+Qq0ufzEQ6HuXDhAslkEo/Hw/z8PKurqzQ0NNDR0cHu3buZnJwkkUhgsVg4ePDghnTx\n5uZmamtr8fv9jI+PU1dXJ9USon89MzPD+vo66XRahsm63W4KhQI+n49QKER1dTVvvPEGra2twFWl\nhd/v32B4tNlC9Pjx46RSKa5cuUJzczPLy8tbbg6KzEGtVnuN7ehWBVipMx58dlSk33vvPex2OwBu\nt5tYLHZXTupeciNFyL1SfzwM17ayp3oj+8rtVt5tbW0A0szI4XDwO7/zO3z22Wcy2LW1tVXqgUVx\nPnHiBDMzM/h8PsbHx6mpqWH//v2yfyyGQwwGA7t27ZI3hvb2doLBIMvLy1itVtra2pidnaW6upoD\nBw5QX1/P8vIy1dXV1NTUkEgkyGazHDlyhHPnzlEul3nnnXekb4dWq6WpqYlLly5RLpex2+0yyFUc\nSyg1MpkMQ0ND9PX1USwWsVqtctNxcnJS+lS7XC4pWSyVStJUKp1Oy175djmCSp3x4LOjIi2KSDgc\n5vjx43zjG9+4Kyf1KPKwXNubMVe62VaIWFnu3r2bgwcPbplcHQ6H+eu//msWFhbIZDJ8/etfl0qG\nWCxGuVymtbWVp556inK5TCKRkOkqlecRCoXk+wm5oMViobe3l3w+j91u5/Lly4TDYZkb2N3dzcLC\nAsPDw+zatQuPx4PJZEKr1fLLX/6SyclJQqEQe/fulT1l0UPOZDKsrKywsrJCNpvF7XZTKpVYX1/f\n0NoQviWVNzMRI7a4uEihUKC1tZXDhw9vW4CVOuPBZscbh9FolN/93d/lT/7kT6iurr4b5/TI8jBc\n2+0er7ezvLxeK6SpqQmNRkNbWxvt7e0bin0wGMThcDA0NMSJEycwGAxMT0/zySef8IUvfIHDhw+T\nTCYB0Gg0nDx5knK5zOjoKF6vVyamABt03KIN4XQ6yWQyeL1egsEgx44dIx6Py8KaTqcZGBigpqYG\np9PJ1NQUxWKRjz76iGQySSwWY3FxUSpPxA3C6/XKeK6VlRU5tNPX1yenJI8ePcrU1BTZbFbmGiYS\nCbk5GAwGMRgMvPDCC8zOznLw4MFrnPEUDw83LNLf+973+PnPf051dTXf/va3+Z3f+R3+83/+zzz7\n7LP34vweah7Ga7vdCnknG1iVhd5qtV5ToCuLfeVmosg7/I//8T/KdBXRux0cHJQyO71eTzQa5Wc/\n+xlut1uaOj3//PNyxNvtdpNKpSiVSrS1tTE3N0dvby+lUgmLxUIgECCTyXDgwAEef/xxjh07Ri6X\nw+Vy4fV6sdlsJJNJuTFpNBo3qDLE2DZANpvdYEEqettw9Qlrs3dGpWpGSBpvhIq5enC5YZF+++23\nefvttwH4r//1v/Lv//2/57nnnrvrJ/Yo8CBc21v5495qhXyjDazrtULgqjRNq9USDAZJpVKyR9vY\n2EhXVxcXLlygubmZmpoahoeH+fDDD0kmkxgMBorFIuFwWIYBFAoFAoEA8XicUqlEV1eX1HHb7Xa6\nu7vJZrPEYjGuXLkiz1fEYRmNRrRaLX19fVKLPT8/T6FQYHV1lcbGRhobG0mn01L9YbFY5PuLz3j4\n8GFp3VrpY1LZmw8Gg9fc3Gpra3fUZ1YxVw82N93uyGQy/PjHP2ZmZoYf/OAHAPybf/Nv+NrXvnbX\nTu5+oHJj8W5tIt6v1/ZO/nFfrwe9VYJ15Uiz6E9fvHgRl8sl9clut5vW1lbeeecd/sf/+B+srKwQ\ni8V47733GB8fx2g0ks1maW9vJ5vN0traSjKZJJvNMjg4iN/vR6vVotVq8Xg8G0IBBgcH0el0nDlz\nRtqkfvWrXwWuBuKKFbbNZpOqDbHhKzY1h4aGKBQKcgWv0+nQ6/WEQiH8fr8Ms63Mctw8ibndza3y\n+ojWz3bfjZLhPdjcdJG2WCwMDg7ezXN5ZLlfr+2d/uPeaoVd6TO9lZRMnINWq2VkZET6f4RCIblZ\n1tvby+/93u/x4YcfYrVaOXnyJFqtFr1eTz6fp729nZWVFbxer/TOOHHiBA6Hg2w2y8mTJ3G5XExN\nTWEymdi3bx9Wq5V0Os3KygqBQICqqio8Hs+WgyBiGjAajWK32+XQicPhwOFw8M///M9kMhmqqqrI\n5/Py8z311FOyAOfz+S1DYXeiNd+Jkkbx4KAmDhXbcjf/uCtjtCoTrDdLycQ5rKysSAtQm82Gw+Gg\nVCrJ1/l8PqmYyGQyuFwuCoUCJpNJFrhDhw4RDAY3eDtbLBZisRhGoxGz2czi4iLT09MybTwej1NT\nU7MhnGArstkswWBQ+lY7HA4KhQIXL16kUCiQSCSYmJgArm5kimSaw4cPUyqVpFPddjFV19tgdTgc\n0s9DtEkqUTK8BxtVpBXbcrf+uCtbGKurq3IjTsjUtjoHYfI/NDSEyWS6ppiXSiV6enqwWq309vbK\nSb1wOEx9fT2ZTIampiZ6e3tJJBJSRTEyMkIqlWJ0dBSdTkdjYyNra2vSgrRQKBCJRORgyVaEQiEu\nX77M8vIy8XicbDbL66+/DoDJZJJKEqvVKjXSgUAAgGQyKUMJCoWCdP27mRuiuBGI8XcRNrudyZIq\nzg8mqkgrrsvd+ONOJBKkUimpI25vb+eZZ56RxvSLi4sbHt3FRprf75fKF7vdLqcaASKRiDRbcrvd\nPPHEEwD84he/kFI4YYgk3rdcLmMwGDh8+DCXL1+mVCrx5JNPcvr0acrlsgycnZub48KFC0xOTm7w\nH6l8Gshms+RyORKJBFNTU3zwwQf4/X76+/ulgZPH4wGgoaEBu90ue+OihbM5NPZGbOXnofrNDx+q\nSN8kynd6I6JAabXaawZMNr+m0rpU/M7q6ioXLlzAbrezuLgoH/Gbm5uJRCIMDg7i9Xo3rAxFsa7s\nxYriNjo6Sj6fp62tjUOHDgHIgZaRkRGMRiMnTpzg4MGD0gc6HA7LNBbRJkgmk7I3Llbv+Xweg8HA\npUuXpAmUmHQ0GAysra3R3t4u2xw+n49cLsfs7CwajYZAIEBNTQ2pVAqNRsOuXbsA6OvrY25uDp/P\nJ1NnxDTkzRZbkQizU/tRxYODKtKKG7KdT7SYCOzp6dmgyhC/s9lzYmBggFQqRS6Xo7W1lfr6erxe\nL4lEgtHRUZaXl2X8lF6vx2g0smfPHpmOLc4jEomwvLxMc3Mzs7OzxGIxnE4nAEajkWQyycDAAMvL\ny1y5cgWPx4PRaJTa6Ewmw/Hjx2lpaUGv1/Pmm29Kn41QKMTa2hp79+5laWmJpaUlUqkUP/zhD2lr\na8PhcPDee+/JKK3e3l7S6TQej4evfvWrjIyMMDU1xfDwMHV1dbhcLl577TVsNts1NzVA3qDExOlO\n+/+q3/zwo4q04rpczydaeE0I46OtVBliI0zomxcWFojFYmSzWR5//HGKxSI1NTW4XC56e3u5ePEi\nuVxObqyJR3hggxQvm81y6dIlenp68Hq9jI2NAdDS0gIgfaxHR0dZXFzEaDRSX19PJBIhl8uxtLTE\n448/LuV0lcVtbGwMh8MhB0qeffZZxsbGqKurY35+nnK5TEdHBx9//DGjo6MyMHZpaYlDhw6xvr5O\nTU2N1FSL99+qgG7Wg99KsVX95ocbVaQV1+V6PtErKyvMz88DV2WEos0A1ypDRIRVLBaT5vciMFar\n1TIwMEAymaShoYFMJsP4+Dj5fB6n0yld9cSNQYSyxmIxcrkcv/Zrv8aBAweAq8qJcDhMIpFAo9HQ\n0NDA3NwcbrdbprKI91xYWJCyusr+srA/XV1dJZ/Pc/z4cWpra3nppZcol8ssLCxw4cIFLBYLOp0O\nh8OB3+/HbDZjtVppaGjg0qVL5HI5WlpayGQyXLx4EYfDgcViuSaRprLAbucJrVbKjy6qSCuuy/V8\noiuHOISZvcBms20wpPf5fBsSrK1W64Z+c+WKMpVK4ff7GRkZkani/f396HQ60uk0mUyGxcVFqqqq\nWFhYkCGs4XCY7373u3Ij7+WXX2ZkZIRgMEgoFMJsNstC/MYbb/DEE0/IgZTKHndnZyfRaJSuri46\nOjr45S9/ic/n49KlSxw+fJjf/M3f5OjRozQ2NrK4uMja2ho2m00msvj9fqlS0Wg0/N//+39Jp9Ms\nLCzwyiuvUFNTc9ODQWpaUKGKtOK6bNfztNls0npT2HRW9lFTqdQ1nhOVCdabV4WVE3SinxyJROjt\n7ZV+F+I86uvr+eEPf4jX6yWXy8n3mJycJBwOA7CyssKxY8fQarUEAgFGR0fJZrP4fD4Z9Lpnzx5p\nWFT5tCAK7JkzZzh37hyZTIaJiQlp2HT48GEaGxvlanl9fV0qN8RnETFV09PTrK+vU1tbK4NgS6XS\nTW8MqmlBhSrSN8lOk8gfJjXIdj3P621abVdcbtQ/rfy9S5cuMTY2RiAQkO8vBlmCwSDJZFJqg1Op\nFDMzMywuLhKNRmlvb6exsZHp6Wn0ej21tbUySstms2E0GuU5bX5aqFzhJ5NJzGYzExMTWCwWOQQj\npG92u52LFy8SCASk0x38ylN7bW2NUqlEOBxGp9NJKeCN8gjF51XTggpVpBW3xXZF91aLixjQOHny\nJPl8nnw+T2dn5wZNdCKRkFI6UcyCwSBOp5Pf/u3f5sMPP6S3t5fW1lZeeeUVWfROnz7NuXPnMJvN\n1NTUbPDB2CpVxu/309DQgNVqJZfL0dbWJp8YHA4HXq+XdDotI6/EzypvNBqNhjfffFOe6+aedCXb\ntTaUeuPRRhVpxV1hu+JyvU0w8bPa2loymQx1dXUsLS3xySef4HQ6WVtbk0Vuc39W/DuNRsMXv/jF\na0Jv4erouNhg3Pwz8f83F0nxGcT4duV5i58dOnRI2qKKQl85PSgMl27ErT59KB5uVJFW3DU2F5fN\nQyibp/fEz0KhECaTCZPJRDQalcVbDIe8/vrr10zX3cyKs9ICdCu2KpLbpXBv/nxbDdjsZHoQNj59\n5PN5OZquCvSjzYMTR6144Kk0BLp06RKfffaZ1D5XFsiqqira2trweDzs27cPm81GLBajurqaQqHA\n4OAg8Xj8mjR10cMVY+c75Xb6v5Xnn0wmKRQK7Nq1S/a+bwZxo+nq6gJgZGREXh/Fo4taSd8FHqZN\nwzuJGAcPBoOUy2U5Aj4xMUFtbe2GNBbRQhCSvJ/97GdMT09jNBoZGxvjwIED/PKXv6S5uRmfz4fF\nYpF66+vJ1a7Xbtm8Gge29Wq+3gbfrU4PinMQgblK0aEAVaQVt8BOhytSqZTMETSZTGSzWTo7O4lE\nIgwNDVEulwkGg1sGzcLVwvXcc89hMBikmiKfz/P+++/j8/lIpVK88soraDQaqdtOp9PXFLeb0RxX\nSgG3e+3NbPDBrU0PgvJ/VmxEFWnFNdxoc28nwxXi9cvLy0xNTfHCCy9gsVjo6uoimUxSLpdlFFap\nVNrSVB9+ZSQkxrhPnDhBNBqlqqpKjqUDXLx4Eb1ez/r6Oo899tiG99iJ5vh6r72Z97nRZt9OVvRq\nFf1oo4q0YgM3KsI7Ha4Qrxc+GhcvXqShoUFO5gWDwZtaMVYWro6ODj755BOKxSKJRGLDpGNXVxdL\nS0tkMhlOnjyJz+e7RgGy1fF2ok/e6mc7uXntZEWvUKgirdhAZRGem5tjYmJiQ1r3Th/FxesjkQga\njQaz2Sx/dqsrRp/PR0dHB263m3g8zhe+8AVqampkMnixWKS2tlaOgW+nAIFfBdxu1cve7ty2+tlW\ngbG3skpXKDajirRiA6KoigQR0S++mcy9rdjs8yFaGzvVAG9efYoBF+ELItjsDyKKceVKWRgqVUr+\nRGp4pbTveue2+Wc7uXmpnrNiJ6gifRfYPEL+IKk9blRUxWt2svKr9Pm4lcKUSqUYHBxkbm6O7u5u\nIpGIDI+tvIEAW/qDXM9u1eFwcOLECWlfumfPHtm+2MkKfyc3L9VzVuwEVaQV13C7RXWuCaY7AAAQ\n0ElEQVS797xeYdquKKZSKX7xi19w9uxZZmZmmJ2dpbW1FZfLtW27YPNN5Hp2q7OzsxgMBp599tkN\n5ko301/efM47uXmpnrPiZlFFWrEld2O1t11hut5GWiKRIJFISK8Nq9XKgQMHZKLJjW4gqVSKdDpN\noVDY0m41FApht9tlBqHdbmdiYoJUKrXlU8TNnLNCcSdRRVqxLfdqtXe9jTRhZjQ9PQ1cVW+0trbS\n2toqk1O2o7KQit/dKjNR2IqKDUQRC6bRaK6xYL2Zc1Yo7iSqSCvuKLeSInK9jTSbzcZLL70k2xCi\nyE5NTfEv//IvuFyubU30NxdSq9V6TSulcjNRKDSamprQaDS0tbXR3t4OXDt5qDb/FPcKVaQVd4xb\nbQHcqLWy2RgpHA7zne98h7m5OVwuF1/4whc2rKpFId+ukFZOQBoMBnmula+3Wq2yQG/1mdTmn+Je\noYq04o5xOy2AnbRWgsEgOp2OQCDA0tISoVCIM2fOMDY2RrlcZu/evTJhfHMhrZyAnJ6e5vnnn5ey\nu9ra2h3pn9Xmn+JeoFzwFHeMu9kCSKVSMnG8trYWq9WKyWQiEAjw9NNPUygUcDgcuFyuDQnjNptt\ng91o5QQkwOzs7IZz3fx61dZQfN6olbTijnGnWwCiZ7zVRODXv/51GXIrVrxic7G5uXnbYiqKbjKZ\nZM+ePVuGA9zNz6RQ7BRVpBV3lDvVAqjsb6+urmIymTZI4jZPGm61ubjd+W3VAtnOkrTyM1W+Dm7d\n5U6h2AmqSCvuSyr727lcjmw2e92Ww+bNxUquN3Rys5udm5NXgA2bjqpQK+4Wqkg/hNyKDO7z4Hrn\nuVlpURkCsJPPdKdc/SpfNzIyQrlcZteuXUojrbjrqCL9kPGgTMJNTU3xwQcfbKtzvlO94BsV4Zvd\nGLxTySsKxU5RRfoh40GYhNtK5yzOc6vVdaVSY6fcqAjf7M1gK5vTB+FpRfHgo4r0Q8aDIBkTOuf6\n+nqCwSDxeHxL4/z+/v4bZhbeDB0dHcDGDcWt+tQ3YvPrVHFW3AtUkX7IuNuSsTvR7xY6Z61WSyAQ\n4Etf+tKWgyM7MdLf7lwri77f79/y39+vLSGFAlSRvicIf+n/8l/+yz053t2ahNuuuO20cPt8vg06\nZyGl2/wUIPw0bvWpYLvWz4PQElIoBKpIK26arYob3Jz38mZ8Pt8GnTNs/RRwO08F27V+HoSWkEIh\nuKUi/Rd/8RcMDAzwv//3/77T5/PIcz9f262K251elW7V973V99uuyKspQsWDxI6L9Pj4OKdPn8Zg\nMNyN83mkeRCu7VabcPfzqnS7Iq/MkRQPCjs2WPqzP/sz3nnnnbtxLo889/O1Ff3okZERxsfH5b8X\nq9L+/n61AadQ3AV2VKTfe+89nnzySQKBwN06n0eW+/3aVrY1SqWS7EfDtc5xCoXiznHTRToej/Pe\ne+/xH/7Df7ib5/NIcj9e20prUFCbbQrF58UNe9Lf+973+PnPf86pU6dob2/nt37rt8jn88zOzvLt\nb3+bP/7jP74X5/lQ8nld2xtJ5raT2t1vm20PikeJQnE73LBIv/3227z99tsb/t38/Dx/9Ed/pAr0\nbfJ5XNubGeTYTrFxP222qYEUxaOCSmZ5xLheb1nwILQ2buZzKBQPA7ekk25oaLgvdbwPA3f72t5M\nAb4fWxubeRBuJArFnUBNHN5DvvGNb/CTn/zkcz2Hnbi+3Y/FWfAg3EgUijuBKtKPIPd7Ab5ZHpbP\noVBcD9WTVigUivsYVaQVCoXiPkYVaYVCobiPUUVaoVAo7mNUkVYoFIr7GFWkFYpt2OxfolB8HigJ\nnkKxBWrsXHG/oFbSimtQK0g1dq64f1AracUG1AryKmrsXHG/oIq0YgMqSfsqauxccb+girRiA2oF\n+SvU2LnifkAVacUG1ApSobi/UEVacQ1qBalQ3D8odYdCoVDcx6girVAoFPcxqkgrFArFfYwq0gqF\nQnEfo4q0QqFQ3MeoIq1QKBT3MXdUglcsFgEIBoN38m0fGsR1EddpJ6hrq1A8vFyvNtzRIr28vAzA\nb/3Wb93Jt33oWF5eprm5ece/A+raKhQPM1vVBk25XC7fqQNks1kGBwfxer3odLo79bYPDcVikeXl\nZfr6+jCbzTv6XXVtFYqHl+vVhjtapBUKhUJxZ1EbhwqFQnEfo4q04hr+4R/+gX/37/4db731Fv/t\nv/037vXD1okTJ+Tx/+iP/ohSqXRPj5/L5fjDP/xDvvKVr9zT4wq+/e1v87WvfY233nqLixcv3vPj\nj46O8vLLL/Puu+/e82MD/Pmf/zlf+9rX+Lf/9t/yL//yL/f02JlMhm984xv89m//Nl/96lf5+OOP\n7+nxt0IVacUGMpkM77//Pv/n//wfvv/97zM5Ocn58+fv6Tl885vf5G/+5m/4/ve/TyqV4tNPP72n\nx//zP/9zenp67ukxBadOnWJmZoa///u/51vf+hbf+ta37unx0+k0f/qnf8rTTz99T48rOHHiBGNj\nY/z93/893/3ud/n2t799T4//8ccf09fXx7vvvstf/dVf8Wd/9mf39PhboVzwFBuwWCz83d/9HXC1\nYCeTSbxe7z09h/feew+73Q6A2+0mFovd0+O/8847xONxfvKTn9zT4wJ89tlnvPzyywC0t7ezurpK\nMpmU1+NuYzQa+du//Vv+9m//9p4cbzNPPPEEe/fuBcDpdJLJZGRK0L3gtddek/9/aWkJv99/T457\nPdRKWrEl3/nOd/jiF7/Iq6++SmNj4z09tihI4XCY48eP8/zzz38ux/88iEQiVFdXy392u91Sfnkv\n0Ov1O1Ye3Ul0Oh1WqxWAH/zgBzz33HOfi5rprbfe4g/+4A/44z/+43t+7M2oIq3Ykv/0n/4TR44c\n4dNPP+Xs2bP3/PjRaJTf/d3f5U/+5E82FK1HjUdVfHXkyBF+8IMf8M1vfvNzOf73/397dxMS1feA\ncfwZHMJfVJiQShQRLUyQQMhFIAqDQYQtKtIBE2pRtPAtKKIETXohq0XposggQic0RCJoU0ELiSRf\ngphVQhASI85kJVMzLabzX4SSb/2L9MwZ+35WznXgOTjeh+O55167unTjxg2dPHky6Z8BJQ1J0r17\n91RVVaXDhw9rYGBAkpSenq7i4mINDw9by6+trVU0GtWRI0dUX1+voqKiJc+enZ9MWVlZikQi06/H\nx8etLzclW19fn27evKn29nbr/74tGAwqFApJkvLy8pRIJDQxMWF1DHMY4CfhcNj4fD4TjUaNMcbU\n1NSYJ0+eWB1DQ0ODefDggdXM2UZHR83evXut5w4NDZlDhw4ZY4wJBoPG7/dbH4MxxrS2tpqOjg7r\nuZOTk6asrMxEIhHr2cYYc+fOHXP+/HljzI9zoaSkxCQSiaSMZQo3s2CO3t5eBQIBeb1e5ebmqrm5\nWR6Px0p2LBZTYWGhCgoKpo+VlZWpoqLCSr4k1dbWamxsTCMjI8rPz1d5ebn27NljLf/q1asaHByU\nx+NRU1OTtm7dai07GAyqpaVF79+/l9frVXZ2ttra2pSRkWElv7u7W21tbdq8efP0sZaWFq1fv95K\nfjweV0NDg0KhkOLxuKqrq+Xz+axkL4SSBgCHsQUvRfDsDmD5+tWzOyjpFBEMBnkCHrDMBQIBbd++\nfcYxSjpFTF3hDwQCysnJsZJ5/fp1KzmADXV1dckewoLGxsZUWVk5704eSjpFTC1x5OTkaMOGDVYy\nr1y58tvvbW5uXsKRAH/P1nnzN+ZbyqSksSiamprmPU55IxkW+n1MRZQ0gJS1nMp4IZQ0ltSfnETM\nurGQf6GMF8Jt4QDgMEoaABxGSQOAwyhpAHAYFw7hDLbxAXMxkwYAhzGTBuCUf3m73XwoaTiPZZDU\nxuf3dyhpAEnBjPn3sCYNAA6jpAHAYSx3AFg0LGEsPmbSAOAwShoAHEZJA4DDWJNGyppv/XOhvbd/\n8l78f6w920NJY1mhPLDcsNwBAA6jpAHAYZQ0ADiMkgYAh3HhEP8sns6GVMBMGgAcRkkDgMMoaQBw\nGCUNAA6jpAHAYZQ0ADiMkgYAh1HSAOAwbmYB8Es8WTC5KGkAkihjV7HcAQAOo6QBwGEsdwCz8OAl\nuISZNAA4jJIGAIdR0gDgMEoaABxGSQOAw9jdAfym+XZ9sOMDS42SBv5CKm7X487C1MJyBwA4jJIG\nAIdR0gDgMEoaABxGSQOAwyhpAHAYJQ0ADqOkAcBhlDQAOIySBgCHcVs4YBHP/8CfoqSBZYzndKQ+\nShpYBijj5Ys1aQBwGCUNAA6jpAHAYaxJA45iJwgkShpYElzIw2KhpIEko9DxK6xJA4DDmEkDKYRZ\n97+HmTQAOIyZdIpIJBKSpLGxsSSPBMBimzqvp87zn1HSKSIcDkuSKisrkzwSAEslHA5r06ZNM455\njDEmSePBH4jH4woGg1q3bp3S0tKSPRwAiyiRSCgcDis/P1/p6ekzvkdJA4DDuHAIAA6jpDHH/fv3\nVV5eLr/fr7Nnz8r2H1v9/f3T+adPn9b379+t5n/79k2nTp3Svn37rOZOuXjxoioqKuT3+/X69Wvr\n+W/evFFpaak6OzutZ0vS5cuXVVFRof379+vx48dWs2OxmOrq6nTw4EEdOHBAz549s5o/H0oaM8Ri\nMT169EiBQEBdXV16+/atXr16ZXUMjY2Nam1tVVdXl758+aK+vj6r+ZcvX1ZeXp7VzCkvX77Uu3fv\n1N3drQsXLujChQtW879+/apz585px44dVnOn9Pf3a2RkRN3d3bp9+7YuXrxoNf/Zs2fKz89XZ2en\nrl27pkuXLlnNnw+7OzDDf//9p7t370r6UdjRaFTr1q2zOobe3l6tWrVKkpSZmamPHz9azT9+/Lg+\nffqkhw8fWs2VpBcvXqi0tFSStGXLFn3+/FnRaHT657HUVqxYofb2drW3t1vJm62wsFDbtm2TJK1Z\ns0axWEyJRMLaxfLdu3dPfx0KhZSdnW0l91eYSWNet27d0s6dO7Vr1y5t3LjRavZUIY2Pj+v58+cq\nKSlJSn4yRCIRrV27dvp1Zmbm9PZLG7xe75zdBTalpaVp5cqVkqSenh4VFxcnZTeT3+/XiRMndObM\nGevZs1HSmNfRo0f19OlT9fX1aWhoyHr+hw8fdOzYMTU1Nc0orX/Nv7r56unTp+rp6VFjY2NS8ru6\nunTjxg2dPHky6Z8BJQ1J0r1791RVVaXDhw9rYGBAkpSenq7i4mINDw9by6+trVU0GtWRI0dUX1+v\noqKiJc+enZ9MWVlZikQi06/Hx8etLzclW19fn27evKn29natXr3aanYwGFQoFJIk5eXlKZFIaGJi\nwuoY5jDAT8LhsPH5fCYajRpjjKmpqTFPnjyxOoaGhgbz4MEDq5mzjY6Omr1791rPHRoaMocOHTLG\nGBMMBo3f77c+BmOMaW1tNR0dHdZzJycnTVlZmYlEItazjTHmzp075vz588aYH+dCSUmJSSQSSRnL\nFG5mwRy9vb0KBALyer3Kzc1Vc3OzPB6PlexYLKbCwkIVFBRMHysrK1NFRYWVfEmqra3V2NiYRkZG\nlJ+fr/Lycu3Zs8da/tWrVzU4OCiPx6OmpiZt3brVWnYwGFRLS4vev38vr9er7OxstbW1KSMjw0p+\nd3e32tratHnz5uljLS0tWr9+vZX8eDyuhoYGhUIhxeNxVVdXy+fzWcleCCUNAA5jTRoAHEZJA4DD\nKGkAcBglDQAOo6QBwGGUNAA4jJIGAIdR0gDgsP8BZLK5vq3b+WkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e201afbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create some normally distributed data\n",
    "mean = [0, 0]\n",
    "cov = [[1, 1], [1, 2]]\n",
    "x, y = np.random.multivariate_normal(mean, cov, 3000).T\n",
    "\n",
    "# Set up the axes with gridspec\n",
    "fig = plt.figure(figsize=(6, 6))\n",
    "grid = plt.GridSpec(4, 4, hspace=0.2, wspace=0.2)\n",
    "main_ax = fig.add_subplot(grid[:-1, 1:])\n",
    "y_hist = fig.add_subplot(grid[:-1, 0], xticklabels=[], sharey=main_ax)\n",
    "x_hist = fig.add_subplot(grid[-1, 1:], yticklabels=[], sharex=main_ax)\n",
    "\n",
    "# scatter points on the main axes\n",
    "main_ax.plot(x, y, 'ok', markersize=3, alpha=0.2)\n",
    "\n",
    "# histogram on the attached axes\n",
    "x_hist.hist(x, 40, histtype='stepfilled',\n",
    "            orientation='vertical', color='gray')\n",
    "x_hist.invert_yaxis()\n",
    "\n",
    "y_hist.hist(y, 40, histtype='stepfilled',\n",
    "            orientation='horizontal', color='gray')\n",
    "y_hist.invert_xaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This type of distribution plotted alongside its margins is common enough that it has its own plotting API in the Seaborn package; see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb) for more details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) | [Contents](Index.ipynb) | [Text and Annotation](04.09-Text-and-Annotation.ipynb) >"
   ]
  }
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